Biology subjects

Ruffini, G.

Publications and source records attributed to Ruffini, G..

At least 37 records · Page 2Linked to original sources

bioRxiv2022

Complex spatiotemporal oscillations emerge from transverse instabilities in large-scale brain networks

Spatiotemporal oscillations underlie all cognitive brain functions. Large-scale brain models, constrained by neuroimaging data, aim to trace the principles underlying such macroscopic neural activity from the intricate and multi-scale structure of the brain. Despite substantial progress in the field, many aspects about the mechanisms behind the onset of spatiotemporal neural dynamics are still unknown. In this work we establish a simple framework for the emergence of complex brain dynamics, including high-dimensional chaos and travelling waves. The model consists of a complex network of 90 brain regions, whose structural connectivity is obtained from tractography data. The activity of each brain area is governed by a Jansen neural mass model and we normalize the total input received by each node so it amounts the same across all brain areas. This assumption allows for the existence of an homogeneous invariant manifold, i.e., a set of different stationary and oscillatory states in which all nodes behave identically. Stability analysis of these homogeneous solutions unveils a transverse instability of the synchronized state, which gives rise to different types of spatiotemporal dynamics, such as chaotic alpha activity. Additionally, we illustrate the ubiquity of this route towards complex spatiotemporal activity in a network of next-generation neural mass models. Altogehter, our results unveil the bifurcation landscape that underlies the emergence of function from structure in the brain. Author summaryMonitoring brain activity with techniques such as EEG and fMRI has revealed that normal brain function is characterized by complex spatiotemporal dynamics. This behavior is well captured by large-scale brain models that incorporate structural connectivity data obtained with MRI-based tractography methods. Nonetheless, it is not yet clear how these complex dynamics emerges from the interplay of the different brain regions. In this paper we show that complex spatiotemporal dynamics, including travelling waves and high-dimensional chaos can arise in simple large-scale brain models through the destabilization of a synchronized oscillatory state. Such transverse instabilities are akin to those observed in chemical reactions and turbulence, and allow for a semi-analytical treatment that uncovers the overall dynamical landscape of the system. Overall, our work establishes and characterizes a general route towards spatiotemporal oscillations in large-scale brain models.

neuroscience↗

bioRxiv2022

LSD-induced increase of Ising temperature and algorithmic complexity of brain dynamics

A topic of growing interest in computational neuroscience is the discovery of fundamental principles underlying global dynamics and the self-organization of the brain. In particular, the notion that the brain operates near criticality has gained considerable support, and recent work has shown that the dynamics of different brain states may be modeled by pairwise maximum entropy Ising models at various distances from a phase transition, i.e., from criticality. Here we aim to characterize two brain states (psychedelics-induced and placebo) as captured by functional magnetic resonance imaging (fMRI), with features derived from the Ising spin model formalism (system temperature, critical point, susceptibility) and from algorithmic complexity. We hypothesized, along the lines of the entropic brain hypothesis, that psychedelics drive brain dynamics into a more disordered state at a higher Ising temperature and increased complexity. We analyze resting state blood-oxygen-level-dependent (BOLD) fMRI data collected in an earlier study from fifteen subjects in a control condition (placebo) and during ingestion of lysergic acid diethylamide (LSD). Working with the automated anatomical labeling (AAL) brain parcellation, we first create "archetype" Ising models representative of the entire dataset (global) and of the data in each condition. Remarkably, we find that such archetypes exhibit a strong correlation with an average structural connectome template obtained from dMRI (r = 0.6). We compare the archetypes from the two conditions and find that the Ising connectivity in the LSD condition is lower than the placebo one, especially at homotopic links (interhemispheric connectivity), reflecting a significant decrease of homotopic functional connectivity in the LSD condition. The global archetype is then personalized for each individual and condition by adjusting the system temperature. The resulting temperatures are all near but above the critical point of the model in the paramagnetic (disordered) phase. The individualized Ising temperatures are higher in the LSD condition than the placebo condition (p = 9 x 10-5). Next, we estimate the Lempel-Ziv-Welch (LZW) complexity of the binarized BOLD data and the synthetic data generated with the individualized model using the Metropolis algorithm for each participant and condition. The LZW complexity computed from experimental data reveals a weak statistical relationship with condition (p = 0.04 one-tailed Wilcoxon test) and none with Ising temperature (r(13) = 0.13, p = 0.65), presumably because of the limited length of the BOLD time series. Similarly, we explore complexity using the block decomposition method (BDM), a more advanced method for estimating algorithmic complexity. The BDM complexity of the experimental data displays a significant correlation with Ising temperature (r(13) = 0.56, p = 0.03) and a weak but significant correlation with condition (p = 0.04, one-tailed Wilcoxon test). This study suggests that the effects of LSD increase the complexity of brain dynamics by loosening interhemispheric connectivity--especially homotopic links. In agreement with earlier work using the Ising formalism with BOLD data, we find the brain state in the placebo condition is already above the critical point, with LSD resulting in a shift further away from criticality into a more disordered state. Author summaryIn this study, we aim to characterize two brain states (psychedelics-induced and placebo), as captured in functional magnetic resonance imaging (fMRI) data, with features derived from the Ising model formalism (system temperature, critical point, susceptibility) and from algorithmic complexity. Under the hypothesis that psychedelics drive the brain into a more disordered state, we study criticality features of brain dynamics under LSD in a within-subject study using the Ising model formalism and algorithmic complexity using Lempel-Ziv and the Block Decomposition methods. Personalized Ising models are created by first using BOLD fMRI data from all the subjects and conditions to create a single Ising "archetype" model--which we can interpret as the average model of the data at unit temperature--and then by adjusting the model temperature for each subject and condition. We find that the effects of LSD translate into increased BOLD signal complexity and Ising temperature, in agreement with earlier findings and predictions from existing theories of the effects of psychedelics, such as the relaxed beliefs under psychedelics (REBUS), the anarchic brain hypothesis [1], and the algorithmic information theory of consciousness (KT) [2, 3]. However, in contrast with some of the previously cited theories, we find that the system in the placebo condition is already in the paramagnetic phase--above the critical point--with ingestion of LSD resulting in a shift away from Ising criticality into a more disordered state. Finally, we highlight the fact that the structural connectome can be recovered to a good degree by fitting an Ising model and that the reduction of homotopic links appears to play an important role in the slide to disorder under psychedelics.

neuroscience↗

bioRxiv2022

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

Cortical function emerges from the interactions of multi-scale networks that may be studied at a high level using neural mass models (NMM), representing the mean activity of large numbers of neurons. In order to properly reproduce experimental data, these models require the addition of further elements. Here we provide a framework integrating conduction physics that can be used to simulate cortical electrophysiology measurements, particularly those obtained from multi-contact laminar electrodes. This is achieved by endowing NMMs with basic physical properties, such as the average laminar location of the apical and basal dendrites of pyramidal cell populations. We call this framework laminar NMM, or LaNMM for short. We then employ this framework to infer the location of oscillatory generators from laminar-resolved data collected from the prefrontal cortex in the macaque monkey. Based on the literature on columnar connectivity, we define a minimal neural mass model capable of generating amplitude and phase coupled slow (alpha/beta, 4-22 Hz) and fast (gamma, 30-250 Hz) oscillations. The synapse layer locations of the two pyramidal cell populations are treated as optimization parameters, together with two more LaNMM-specific parameters, to compare the models with the multi-contact recordings. We rank the candidate models using an optimization function that evaluates the match between the functional connectivity (FC) of the model and data, where the FC is defined by the covariance between bipolar voltage measurements at different cortical depths. The family of best solutions reproduces the FC of the observed electrophysiology while selecting locations of pyramidal cells and their synapses that result in the generation of fast activity at superficial layers and slow activity across most depths, in line with recent literature proposals. HighlightsO_LIWe provide a neural mass modeling formalism that includes a physical layer to simulate electrophysiology measurements. C_LIO_LITo analyze in-vivo data collected in the macaque monkey during a memory task, we propose a specific model with two coupled main circuits that can generate realistic electrophysiological signals in two important oscillatory regimes--the alpha/beta and the gamma bands. C_LIO_LIPhysical elements in the model shed light on the generation of oscillations in the two regimes and on the relative power distribution of fast and slow oscillatory signals across cortical depth, which we show can be altered by the choice of the reference location or method. C_LIO_LIThe model is contrasted with in-vivo data, with parameters adjusted by matching voltage statistics in the alpha/beta and gamma bands, leading to a solution with slow frequency components generated by synapses spanning most cortical layers and fast oscillations in superficial layers. C_LIO_LIThe resulting formalism provides useful tools and concepts to analyze and model data, with implications for understanding altered oscillatory EEG activity in dementia, Alzheimers disease and other disorders with oscillatory features. C_LI

neuroscience↗

bioRxiv2022

A spherical harmonics-based framework for representing steady-state shifts in neuron models induced by weak electric fields

ObjectiveWe provide a systematic framework for quantifying the effect of externally applied weak electric fields on realistic neuron compartment models as captured by physiologically relevant quantities such as the membrane potential or transmembrane current as a function of the orientation of the field. ApproachWe define a response function as the steady-state change of the membrane potential induced by a canonical external field of 1 V/m as a function of its orientation. We estimate the function values through simulations employing reconstructions of the rat somatosensory cortex from the Blue Brain Project. The response of different cell types is simulated using the NEURON simulation environment. We represent and analyze the angular response as an expansion in spherical harmonics. Main resultsWe report membrane perturbation values comparable to those in the literature, extend them to different cell types, and provide their profiles as spherical harmonic coefficients. We show that at rest, responses are dominated by their dipole terms ({ell} = 1), in agreement with experimental findings and compartment theory. Indeed, we show analytically that for a passive cell, only the dipole term is nonzero. However, while minor, other terms are relevant for states different from resting. In particular, we show how{ell} = 0 and{ell} = 2 terms can modify the function to induce asymmetries in the response. SignificanceThis work provides a practical framework for the representation of the effects of weak electric fields on different neuron types and their main regions--an important milestone for developing micro- and mesoscale models and optimizing brain stimulation solutions.

neuroscience↗

bioRxiv2022

Towards a mesoscale physical modeling framework for stereotactic-EEG recordings

ObjectiveStereotactic-EEG (SEEG) and scalp EEG recordings can be modeled using mesoscale neural mass population models (NMM). However, the relationship between those mathematical models and the physics of the measurements is unclear. In addition, it is challenging to represent SEEG data by combining NMMs and volume conductor models due to the intermediate spatial scale represented by these measurements. ApproachWe provide a framework combining the multicompartmental modeling formalism and a detailed geometrical model to simulate the transmembrane currents that appear in layer 3, 5 and 6 pyramidal cells due to a synaptic input. With this approach, it is possible to realistically simulate the current source density (CSD) depth profile inside a cortical patch due to inputs localized into a single cortical layer and the induced voltage measured by two SEEG contacts using a volume conductor model. Based on this approach, we built a framework to connect the activity of a NMM with a volume conductor model and we simulated an example of SEEG signal as a proof of concept. Main resultsCSD depends strongly on the distribution of the synaptic inputs onto the different cortical layers and the equivalent current dipole strengths display substantial differences (of up to a factor of four in magnitude in our example). Thus, the inputs coming from different neural populations do not contribute equally to the electrophysiological recordings. A direct consequence of this is that the raw output of neural mass models is not a good proxy for electrical recordings. We also show that the simplest CSD model that can accurately reproduce SEEG measurements can be constructed from discrete monopolar sources (one per cortical layer). SignificanceOur results highlight the importance of including a physical model in NMMs to represent measurements. We provide a framework connecting microscale neuron models with the neural mass formalism and with physical models of the measurement process that can improve the accuracy of predicted electrophysiological recordings.

neuroscience↗

bioRxiv2022

Comparison between an exact and a heuristic neural mass model with second order synapses

Neural mass models (NMMs) are designed to reproduce the collective dynamics of neuronal populations. A common framework for NMMs assumes heuristically that the output firing rate of a neural population can be described by a static nonlinear transfer function (NMM1). However, a recent exact mean-field theory for quadratic integrate-and-fire (QIF) neurons challenges this view by showing that the mean firing rate is not a static function of the neuronal state but follows two coupled non-linear differential equations (NMM2). Here we analyze and compare these two descriptions in the presence of second-order synaptic dynamics. First, we derive the mathematical equivalence between the two models in the infinitely slow synapse limit, i.e., we show that NMM1 is an approximation of NMM2 in this regime. Next, we evaluate the applicability of this limit in the context of realistic physiological parameter values by analyzing the dynamics of models with inhibitory or excitatory synapses. We show that NMM1 fails to reproduce important dynamical features of the exact model, such as the self-sustained oscillations of an inhibitory interneuron QIF network. Furthermore, in the exact model but not in the limit one, stimulation of a pyramidal cell population induces resonant oscillatory activity whose peak frequency and amplitude increase with the self-coupling gain and the external excitatory input. This may play a role in the enhanced response of densely connected networks to weak uniform inputs, such as the electric fields produced by non-invasive brain stimulation.

neuroscience↗

bioRxiv2022

Towards the identification and optimization of the "dose-response" relationship of transcranial direct current stimulation

Transcranial direct current stimulation (tDCS) is being investigated as a therapeutic tool in several neuropsychiatric disorders, but its mechanisms of action remain incompletely understood and its effectiveness is highly variable from subject to subject. Some of this inter-subject variance is thought to stem from individual differences in head anatomy, which result in different electric field distributions across individuals for the same stimulation settings. In a recent block-randomized, sham-controlled pilot study, 18 older adults with mild-to-moderate motor and cognitive impairments underwent a 2-week, 10-session intervention of 2 mA bipolar tDCS with the anode over the left dorsolateral prefrontal cortex (DLPFC) and cathode over the right supraorbital region (anodal F3 vs. FP2 using sponge electrodes in the 10-20 EEG system). tDCS, compared to sham stimulation, improved average cognitive-motor performance over a two-week follow-up. For a subset of 12 participants from this cohort who underwent structural brain MRIs at baseline, we derived tDCS-induced electric field properties using MRI-derived realistic, individualized finite element modeling. We hypothesized that the electric field component orthogonal (En) to the cortex over the left DLPFC would correlate with observed cognitive-motor improvements. Across the 12 subjects the average tDCS-induced electric field over the target area was highly variable with a standard deviation of the surface average En over the left DLPFC of 21% of the mean. For those subjects who were randomized to tDCS intervention (n=6), those who exhibited greater improvement in cognitive-motor performance were exposed to greater normal component of the tDCS electric field averaged over the left DLPFC (r95 % [isin] [-0.99,-0.59]). In contrast, averaged over the right DLPFC, En was a poor predictor of outcome (r95 % [isin] [-0.08,0.97]), as was the amplitude of the current injected (a widely adopted dosing parameter). In light of these results, we conducted a retrospective modeling experiment using personalized montage optimization algorithms to achieve a desired En amplitude over the left DLPFC. The optimized montages induced an average En on the left DLPFC comparable to the bipolar montages, but with half the variability (standard deviation was 11% compared to 21% with bipolar montages). In addition, these results suggest that optimized multichannel montages afford more focal targeting, with lower "spill-over" of electric field onto other cortical areas. These proof-of-concept results, though based upon a small sample size, indicate that assessment of the E-field distribution is critical when evaluating the effects of tDCS intervention. They also reinforce the importance of controlled cortical electric field dosing in tDCS and the potential advantages of optimized multichannel stimulation. Main findings: O_LIThe lDPFC averaged normal component of the electric field correlated with cognitive-motor improvements following tDCS intervention. C_LIO_LIIn older adults, head and brain anatomical differences resulted in high inter-subject variability of tDCS-induced electric fields. C_LIO_LItDCS montages optimized from modeling and delivered via multichannel electrode arrays can control variance and maximize electric field on target, while minimizing effects elsewhere. C_LI

neuroscience↗

bioRxiv2021

A personalizable autonomous neural mass model of epileptic seizures

Work in the last two decades has shown that neural mass models (NMM) can realistically reproduce and explain epileptic seizure transitions as recorded by electrophysiological methods (EEG, SEEG). In previous work, advances were achieved by increasing excitation and heuristically varying network inhibitory coupling parameters in the models. Based on these early studies, we provide a laminar NMM capable of realistically reproducing the electrical activity recorded by SEEG in the epileptogenic zone during interictal to ictal states. With the exception of the external noise input into the pyramidal cell population, the model dynamics are autonomous. By setting the system at a point close to bifurcation, seizure-like transitions are generated, including pre-ictal spikes, low voltage fast activity, and ictal rhythmic activity. A novel element in the model is a physiologically motivated algorithm for chloride dynamics: the gain of GABAergic post-synaptic potentials is modulated by the pathological accumulation of chloride in pyramidal cells due to high inhibitory input and/or dysfunctional chloride transport. In addition, in order to simulate SEEG signals for comparison with real seizure recordings, the NMM is embedded first in a layered model of the neocortex and then in a realistic physical model. We compare modeling results with data from four epilepsy patient cases. By including key pathophysiological mechanisms, the proposed framework captures succinctly the electrophysiological phenomenology observed in ictal states, paving the way for robust personalization methods based on NMMs.

neuroscience↗

bioRxiv2021

Modeling implanted metals in electrical stimulation applications

BackgroundMetal implants impact the dosimetry assessment in electrical stimulation techniques. Therefore, they need to be included in numerical models. While currents in the body are ionic, metals only allow electron transport. In fact, charge transfer between tissues and metals requires electric fields to drive the electrochemical reactions at the interface. Thus, metal implants may act as insulators or as conductors depending on the scenario. Objective/HypothesisThe aim of this paper is to provide a theoretical argument that guides the choice of the correct representation of metal implants using purely electrical models but considering the electrochemical nature of the problem in the technology of interest. MethodsWe built a simple model of a metal implant exposed to a homogeneous electric field of various magnitudes to represent both weak (e.g., tDCS), medium (TMS) or strong field stimulation. The same geometry was solved using two different models: a purely electric one (with different conductivities for the implant), and an electrochemical one. As an example of application, we also modeled a transcranial electrical stimulation (tES) treatment in a realistic head model with a skull plate using a high and low conductivity value for the plate. ResultsMetal implants generally act as electric insulators when exposed to electric fields up to around 100 V/m (tES and TMS range) and they only resemble a perfect conductor for fields in the order of 1000 V/m and above. The results are independent of the implants metal, but they depend on its geometry. Conclusion(s)Metal implants can be accurately represented by a simple electrical model of constant conductivity, but an incorrect model choice can lead to large errors in the dosimetry assessment. In particular, tES modeling with implants incorrectly treated as conductors can lead to errors of 50% in induced fields or more. Our results can be used as a guide to select the correct model in each scenario.

bioengineering↗

bioRxiv2021

The 2D Ising model, criticality and AIT

In this short note we study the 2D Ising model, a universal computational model which reflects phase transitions and critical phenomena, as a framework for establishing links between systems that exhibit criticality with the notions of complexity. This is motivated in the context of neuroscience applications stemming from algorithmic information theory (AIT). Starting with the original 2D Ising model, we show that -- together with correlation length of the spin lattice, susceptibility to a uniform external field -- the correlation time of the magnetization time series, the compression ratio of the spin lattice, the complexity of the magnetization time series -- as derived from Lempel-Ziv-Welch compression--, and the rate of information transmission in the lattice, all reflect the effects of the phase transition, which results in spacetime pockets of uniform magnetization at all scales. We also show that in the Ising model the insertion of sparse long-range couplings has a direct effect on the critical temperature and other parameters. The addition of positive links extends the ordered regime to higher critical temperatures, while negative links have a stronger, disordering influence at the global scale. We discuss some implications for the study of long-range (e.g., ephaptic) interactions in the human brain and the effects of weak perturbations in neural dynamics.

neuroscience↗

bioRxiv2021

Analysis and extension of exact mean-field theory with dynamic synaptic currents

Neural mass models such as the Jansen-Rit or Wendling systems provide a practical framework for representing and interpreting electro-physiological activity (1-6) in both local and global brain models (7, 8). However, they are only partly derived from first principles. While the post-synaptic potential dynamics are inferred from data and can be grounded on diffusion physics (9-11), Freemans "wave to pulse" (W2P) sigmoid function (12-14), used to transduce mean population membrane potential into firing rate, rests on a weaker theoretical standing. On the other hand, Montbrio et al (15, 16) derive an exact mean-field theory (MPR) from a quadratic integrate and fire neuron model under some simplifying assumptions, thereby connecting microscale neural mechanisms and meso/macroscopic phenomena. The MPR model can be seen to replace Freemans W2P sigmoid function with a pair of differential equations for the mean membrane potential and firing rate variables--a dynamical relation between firing rate and membrane potential--, providing a more fundamental interpretation of the semi-empirical NMM sigmoid parameters. In doing so, we show it sheds light on the mechanisms behind enhanced network response to weak but uniform perturbations. In the exact mean-field theory, intrinsic population connectivity modulates the steady-state firing rate W2P relation in a monotonic manner, with increasing self-connectivity leading to higher firing rates. This provides a plausible mechanism for the enhanced response of densely connected networks to weak, uniform inputs such as the electric fields produced by non-invasive brain stimulation. This new, dynamic W2P relation also endows the neural mass model with a form of "inertia", an intrinsic delay to external inputs that depends on, e.g., self-coupling strength and state of the system. Next, we complete the MPR model by adding the second-order equations for delayed post-synaptic currents and the coupling term with an external electric field, bringing together the MPR and the usual NMM formalisms into a unified exact mean-field theory (NMM2) displaying rich dynamical features. In the single population model, we show that the resonant sensitivity to weak alternating electric field is enhanced by increased self-connectivity and slow synapses. O_TEXTBOXSignificance Several decades of research suggest that weak electric fields influence neural processing. A long-standing question in the field is how networks of neurons process spatially uniform weak inputs that barely affect a single neuron but that produce measurable effects in networks. Answering this can help implement electric field coupling mechanisms in neural mass models of the whole brain, and better represent the impact of electrical stimulation or ephaptic communication. This issue can be studied using local detailed computational models, but the use of statistical mechanics methods can deliver "mean-field models" to simplify the analysis. Following the steps of Montbrio et al (15, 16), we show that the sensitivity to inputs such a weak alternating electric field can be modulated by the intrinsic self-connectivity of a neural population, and produce a more grounded set of equations for neural mass modeling to guide further work. C_TEXTBOX

neuroscience↗

bioRxiv2020

Multifocal tDCS modulates resting-state functional connectivity in older adults depending on induced electric field and baseline connectivity

BackgroundAdvancing age affects the brains resting-state functional networks. Combining non-invasive brain stimulation (NIBS) with neuroimaging is a promising approach to modulate activity across resting-state functional systems and explore their true contribution to cognitive function in aging. However, substantial individual variability in the response to NIBS has been reported and, hence, identifying the individual predictors of NIBS-induced modulatory effects is crucial if we are to harness their potential. MethodsThirty-one cognitively healthy older adults (71.68 {+/-} 2.5 years; 19 females) underwent two different multifocal real tDCS conditions (C1 and C2) and a sham condition in a crossover design during a resting-state functional magnetic resonance imaging (rs-fMRI) acquisition. The real tDCS conditions were designed to induce two distinct electric field distribution patterns either targeting generalized cortical overactivity or a dissociation between the frontal areas and the posteromedial cortex. Stimulation was delivered through an MRI-compatible device using 8 small circular electrodes. Each individuals anatomical T1-weighted MRI was used to generate a finite element model to define the individual electric field generated by each tDCS condition. ResultsThe two tDCS conditions modulated resting-state connectivity differently. C1 increased the coactivation of numerous functional couplings as compared to sham, however, a smaller amount of connections increased in C1 as compared to C2, while no differences between C2 and sham were appreciated. At the group level, C1-induced modulations primarily included temporo-occipital areas and distinct cerebellar regions. This functional pattern was anatomically consistent with the estimated distribution of the induced electric field in the C1 condition. Finally, at the individual level, the extent of tDCS-induced rs-fMRI modulation in C1 was predicted by baseline resting-state connectivity and simulation-based electric field magnitude. DiscussionOur results highlighted that multifocal tDCS procedures can effectively change neural dynamics in the elderly consistently with the spatial distribution of the estimated electric fields on the brain. Furthermore, we showed that specific brain factors that have been revealed to explain part of the individual variability to NIBS in young samples are also relevant in older adults. In accordance, designing multifocal tDCS configurations based on specific fMRI patterns appears to be a valuable approach to precisely adjust those complex neural dynamics sustaining cognition that are affected as a function of age. Furthermore, these innovative NIBS-based interventions should be individually-tailored based on subject-specific structural and functional data to ultimately boost their potential in aged populations.

neuroscience↗

bioRxiv2020

Control of endoplasmic reticulum membrane biogenesis by regulators of lipid metabolism

Cells dynamically adapt organelle size to current physiological demand. Organelle growth requires membrane biogenesis and therefore needs to be coordinated with lipid metabolism. The endoplasmic reticulum (ER) can undergo massive expansion, but the regulatory mechanisms that govern ER membrane biogenesis are largely unclear. Here, we conduct a genetic screen for factors involved in ER membrane expansion in budding yeast and identify the ER transmembrane protein Ice2 as a strong hit. We show that Ice2 promotes ER membrane biogenesis by opposing the phosphatidic acid phosphatase Pah1, which is called lipin in metazoa. Ice2 inhibits the conserved Nem1-Spo7 complex, which dephosphorylates and thus activates Pah1. Furthermore, Ice2 cooperates with the transcriptional regulation of lipid synthesis genes and helps to maintain ER homeostasis during ER stress. These findings establish inhibition of the lipin phosphatase complex as an important mechanism for controlling ER membrane biogenesis.

cell biology↗

bioRxiv2020

Convolutional neural network MRI segmentation for fast and robust optimization of transcranial electrical current stimulation of the human brain

The segmentation of structural MRI data is an essential step for deriving geometrical information about brain tissues. One important application is in transcranial electrical stimulation (e.g., tDCS), a non-invasive neuromodulatory technique where head modeling is required to determine the electric field (E-field) generated in the cortex to predict and optimize its effects. Here we propose a deep learning-based model (StarNEt) to automatize white matter (WM) and gray matter (GM) segmentation and compare its performance with FreeSurfer, an established tool. Since good definition of sulci and gyri in the cortical surface is an important requirement for E-field calculation, StarNEt is specifically designed to output masks at a higher resolution than that of the original input T1w-MRI. StarNEt uses a residual network as the encoder (ResNet) and a fully convolutional neural network with U-net skip connections as the decoder to segment an MRI slice by slice. Slice vertical location is provided as an extra input. The model was trained on scans from 425 patients in the open-access ADNI+IXI datasets, and using FreeSurfer segmentation as ground truth. Model performance was evaluated using the Dice Coefficient (DC) in a separate subset (N=105) of ADNI+IXI and in two extra testing sets not involved in training. In addition, FreeSurfer and StarNEt were compared to manual segmentations of the MRBrainS18 dataset, also unseen by the model. To study performance in real use cases, first, we created electrical head models derived from the FreeSurfer and StarNEt segmentations and used them for montage optimization with a common target region using a standard algorithm (Stimweaver) and second, we used StarNEt to successfully segment the brains of minimally conscious state (MCS) patients having suffered from brain trauma, a scenario where FreeSurfer typically fails. Our results indicate that StarNEt matches FreeSurfer performance on the trained tasks while reducing computation time from several hours to a few seconds, and with the potential to evolve into an effective technique even when patients present large brain abnormalities.

bioinformatics↗

bioRxiv2019

Realistic modeling of ephaptic fields in the human brain

Several decades of research suggest that weak electric fields may influence neural processing, including those induced by neuronal activity and recently proposed as substrate for a potential new cellular communication system, i.e., ephaptic transmission. Here we aim to map ephaptic activity in the human brain and explore its trajectory during aging by characterizing the macroscopic electric field generated by cortical dipoles using realistic finite element modeling. We find that modeled endogenous field magnitudes are comparable to those in measurements of weak but functionally relevant endogenous fields and to those generated by noninvasive transcranial brain stimulation, therefore possibly able to modulate neuronal activity. Then, to evaluate the role of self-generated ephaptic fields in the human cortex, we adapt an interaction approximation that considers the relative orientation of neuron and field to derive the membrane potential perturbation in pyramidal cells. Building on this, we define a simplified metric (EMOD1) that weights dipole coupling as a function of distance and relative orientation between emitter and receiver and evaluate it in a sample of 401 realistic human brain models from subjects aged 16-83. Results reveal that ephaptic modulation follows gyrification patterns in the human brain, and significantly decreases with age, with higher involvement of sensorimotor regions and medial brain structures. By providing the means for fast and direct interaction between neurons, ephaptic modulation likely contributes to the complexity of human function for cognition and behavior, and its modification across the lifespan and in response to pathology.

neuroscience↗

bioRxiv2019

ESCRT machinery mediates selective microautophagy of endoplasmic reticulum

ER-phagy, the selective autophagy of endoplasmic reticulum (ER), safeguards organelle homeostasis by eliminating misfolded proteins and regulating ER size. ER-phagy can occur by macroautophagic and microautophagic mechanisms. While dedicated machinery for macro-ER-phagy has been discovered, the molecules and mechanisms mediating micro-ER-phagy remain unknown. Here, we first show that micro-ER-phagy in yeast involves the conversion of stacked cisternal ER into multilamellar ER whorls during microautophagic uptake into lysosomes. Second, we identify the conserved Nem1-Spo7 phosphatase complex and ESCRT proteins as key components for micro-ER-phagy. Third, we demonstrate that macro- and micro-ER-phagy are parallel pathways with distinct molecular requirements. Finally, we provide evidence that ESCRT proteins directly function in scission of the lysosomal membrane to complete the microautophagic uptake of ER. These findings establish a framework for a mechanistic understanding of micro-ER-phagy and, thus, a comprehensive appreciation of the role of autophagy in ER homeostasis.

cell biology↗

bioRxiv2019

Resting Eyes Closed Beta-Phase High Gamma-Amplitude Coupling Deficits in Children with Attention Deficit Hyperactivity Disorder

ObjectiveAttention-deficit hyperactivity disorder (ADHD) is the neurobehavioral disorder with the largest prevalence rate in childhood. ADHD is generally assessed based on physical examination of the child and interviews, and therefore prone to subjectivity. This fact may lead to a high risk of mis- and over-diagnosis, a problem that can be addressed through the use of objective markers.\n\nMethodsHere we propose to use phase-amplitude coupling as a digital biomarker in ADHD. We investigated the hypothesis that coupling between the phase of slow brain rhythms and the amplitude of fast rhythms is altered in the ADHD population. We tested this hypothesis measuring phase-amplitude coupling (PAC) in the 4 to 200Hz range in electroencephalographic (EEG) signals recorded in the central-frontal area in children during eyes closed resting state.\n\nResultsUsing automatic clustering, we observed statistically significant beta-gamma PAC deficits in the ADHD population in the frontal-left hemisphere.\n\nConclusionsThese findings suggests alterations in the beta-gamma coupling in the ADHD population. We discuss the hypothesis that these alterations may be indicators of working memory and attention deficits.\n\nSignificanceThe study of the coupling between the different brain rhythms can potentially contribute to the understanding and clinical diagnosis of ADHD.

neuroscience↗

bioRxiv2019

Evaluating Complexity of Fetal MEG Signals: A Comparison of Different Metrics and Their Applicability

In this work, we aim to investigate whether information based metrics of neural activity are a useful tool for the search for consciousness before and shortly after birth. Neural activity is measured using fetal magnetoencephalography (fMEG) in human fetuses and neonates. Based on recent theories on consciousness, information-based metrics are established to measure brain complexity and to assess different levels of consciousness. Different metrics (measures of entropy, compressibility and fractality) are, thus, explored in a reference population and their usability is evaluated. For comparative analysis, two fMEG channels were selected: one where brain activity was previously detected and one at least 15cm away, that represented a control channel. The usability of each metric was evaluated and results from the brain and control channel were compared. Concerning the ease of use with fMEG data, Lempel-Ziv-Welch (LZW) compression was evaluated as best, as it is unequivocal and needs low computational effort. The fractality measures have a high parameter space and therefore forfeit comparability, while entropy measures require a higher computational effort and more parameters to adjust compared to LZW. Comparison of a channel with brain activity and a control channel in neonatal recordings showed significant differences in most complexity metrics. This clear difference can be seen as proof of concept for the usability of complexity metrics in fMEG. For fetal data, this comparison produced less clear results which can be related to leftover maternal signals included in the control channel. Further work is necessary to conclusively interpret results from the analysis of fetal recordings. Yet this study shows that complexity metrics can be used for fMEG data on early consciousness and the evaluation gives a guidance for future work. The inconsistency of results from different metrics highlights the challenges of working with complexity metrics as neural correlates of consciousness, as well as the caution one should apply to interpret them.

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