Biology subjects

Salvador, R.

Publications and source records attributed to Salvador, R..

30 records · Page 2Linked to original sources

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

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

Determination of HLA-DR level in cytotoxic T lymphocytes: a new validated tool to predict breast cancer response to treatment

BackgroundNeoadjuvant chemotherapy (NACT) is the usual treatment for locally-advanced breast cancer (BC). However, more than half of the patients lack an effective response to this treatment. Thus, its urgent to find predictive biomarkers. Recently, we proposed the HLA-DR expression level in cytotoxic T lymphocytes (CTLs) as a robust biomarker to select, in advance, patients that will actually benefit from NACT. Patients and MethodsA total of 202 BC patients, 102 of which submitted to NACT, were enrolled in this study. 61 biopsies and 41 blood samples collected pre-NACT and 100 non-NACT tumor samples were immunophenotyped by flow cytometry. Both NACT and non-NACT patients were followed up for 34 months. Blood-isolated immune cells were cultured with BC cell lines in a 3D system. ResultsHere we confirmed that HLA-DR level in CTLs is a highly sensitive and specific biomarker to predict BC response to NACT, reflected in circulation and independent of the patients age, BC subtype and other tumor-immunological features. Therefore, we developed a predictive probability model, based on the determination of HLA-DR level in tumor-infiltrating CTLs, that could be used to guide therapeutic decisions. Interestingly, this biomarker was also associated with progression-free survival, regardless the treatment. Contrary to HLA-DRnegative CTLs, HLA-DR+ CTLs were able to reduce the viability of tumor cells, in culture, in agreement with their higher expression of activation, proliferation and cytotoxicity-related molecules. Tissue-residency and memory markers were also increased in HLA-DR+ CTLs. These anti-tumor features of HLA-DR+ CTLs may justify the clinical observations. ConclusionHLA-DR level in CTLs is a validated and independent biomarker to predict response to NACT which allow the establishment of a clinical meaningful tool to select in advance patients that will truly benefit from this treatment. Intriguingly, it may be further used as a biomarker of BC patients general prognosis. Highlights- HLA-DR level in cytotoxic T cells is an independent predictive factor of breast cancer response to NACT - A predictive probability model based on this biomarker was developed as a new tool to improve treatment decisions - HLA-DR level in cytotoxic T cells is also reflected systemically - HLA-DR level in cytotoxic T cells is also a prognostic factor, associated with progression-free survival - HLA-DR+ cytotoxic T cells exhibit several phenotypic and functional anti-tumor characteristics

immunology↗

bioRxiv2021

Circulating Low Density Neutrophils of Breast Cancer Patients are Associated with their Worse Prognosis due to the Impairment of T cell Responses

Neutrophils are prominent immune components of solid tumors, which can protect against the onset of cancer (N1) or have pro-tumor activity (N2). Circulating neutrophils, divided into high density neutrophils (HDN) and low density neutrophils (LDN), functionally mirror those N1 and N2 cells, respectively. LDN, a rare subset in non-pathological conditions, have been extensively studied in cancer, due to their frequency in this disease and their pro-tumor phenotype. However, this has been mainly demonstrated in animal models and proper validation in humans is an urgent need. Here, we further enlighten the clinical impact of LDN in a cohort of breast cancer (BC) patients. We observed that LDN were practically absent in healthy donors blood, while were significantly increased in the blood of BC patients, particularly with metastatic disease. Relevant for a clinical translation, within the population of non-metastatic patients, LDN were more prevalent in patients with poor response to neoadjuvant chemotherapy than in responders. The association of a higher incidence of circulating LDN and the worse prognosis of BC patients could be explained by the pro-tumor/immunosuppressive characteristics exhibited by these cells. Namely, there are more LDN expressing the immunosuppressive marker PD-L1, than HDN. Additionally, LDN also showed increased expression of activation markers; a robust formation of neutrophil extracellular traps; an augmented phagocytic activity; and a higher capacity to release reactive oxygen species, which may contribute for tumor development and metastization. Moreover, the percentage of LDN in BC patients blood was negatively correlated with activated cytotoxic T lymphocytes and positively correlated with the immunosuppressive CCR4+ regulatory T cells, corroborating their impairment on the anti-tumor immune responses, which was further demonstrated ex vivo. Hence, this study reveals the potential of LDN as a clinical meaningful biomarker of BC response to treatment and opens new avenues for developing targeted immunotherapies.

immunology↗

bioRxiv2020

A Method for the Automatic Normalization and 3D Facial Mesh Extraction from Head Magnetic Resonance Imaging

The analysis of 3D facial shape in medicine is motivated by the fact that certain diseases and syndromes are associated to specific facial dysmorphologies. In this context, 3D facial shape analysis constitutes a promising and non-invasive support to traditional diagnostic methods. In this work, we explore the use of head magnetic resonances to obtain accurate 3D facial meshes that enable subsequent facial shape analysis. We present a fully automatic method that normalizes the orientation and alignment of 3D point clouds corresponding to head magnetic resonances by detecting salient facial features. Moreover, using clustering techniques, our method also allows to eliminate noise and artifacts appearing in magnetic resonance imaging. Finally, through bidirectional ray tracing, we obtain a dense 3D facial mesh that accurately captures facial shape. The proposed method has been built and evaluated on a dataset of 185 head magnetic resonances, and it has demonstrated its ability to successfully orient, align and obtain a dense 3D facial mesh with a high accuracy rate.

bioinformatics↗

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

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↗

bioRxiv2018

Personalization of hybrid brain models from neuroimaging and electrophysiology data

Personalization is rapidly becoming standard practice in medical diagnosis and treatment. This study is part of an ambitious program towards computational personalization of neuromodulatory interventions in neuropsychiatry. We propose to model the individual human brain as a network of neural masses embedded in a realistic physical matrix capable of representing measurable electrical brain activity. We call this a hybrid brain model (HBM) to highlight that it encodes both biophysical and physiological characteristics of an individual brain. Although the framework is general, we provide here a pipeline for the integration of anatomical, structural and functional connectivity data obtained from magnetic resonance imaging (MRI), diffuse tensor imaging (DTI connectome) and electroencephalography (EEG). We personalize model parameters through a comparison of simulated cortical functional connectivity with functional connectivity profiles derived from cortically-mapped, subject-specific EEG. We show that individual information can be represented in model space through the proper adjustment of two parameters (global coupling strength and conduction velocity), and that the underlying structural information has a strong impact on the functional outcome of the model. These findings provide a proof of concept and open the door for further advances, including the model-driven design of non-invasive brain-stimulation protocols.

neuroscience↗