Your browser doesn't support javascript.
loading
Mostrar: 20 | 50 | 100
Resultados 1 - 20 de 193
Filtrar
1.
PLoS Comput Biol ; 20(7): e1011642, 2024 Jul 11.
Artículo en Inglés | MEDLINE | ID: mdl-38990984

RESUMEN

The Virtual Epileptic Patient (VEP) refers to a computer-based representation of a patient with epilepsy that combines personalized anatomical data with dynamical models of abnormal brain activities. It is capable of generating spatio-temporal seizure patterns that resemble those recorded with invasive methods such as stereoelectro EEG data, allowing for the evaluation of clinical hypotheses before planning surgery. This study highlights the effectiveness of calibrating VEP models using a global optimization approach. The approach utilizes SaCeSS, a cooperative metaheuristic capable of parallel computation, to yield high-quality solutions without requiring excessive computational time. Through extensive benchmarking on synthetic data, our proposal successfully solved a set of different configurations of VEP models, demonstrating better scalability and superior performance against other parallel solvers. These results were further enhanced using a Bayesian optimization framework for hyperparameter tuning, with significant gains in terms of both accuracy and computational cost. Additionally, we added a scalable uncertainty quantification phase after model calibration, and used it to assess the variability in estimated parameters across different problems. Overall, this study has the potential to improve the estimation of pathological brain areas in drug-resistant epilepsy, thereby to inform the clinical decision-making process.

2.
J Comput Neurosci ; 2024 Jul 05.
Artículo en Inglés | MEDLINE | ID: mdl-38967732

RESUMEN

We derive a next generation neural mass model of a population of quadratic-integrate-and-fire neurons, with slow adaptation, and conductance-based AMPAR, GABAR and nonlinear NMDAR synapses. We show that the Lorentzian ansatz assumption can be satisfied by introducing a piece-wise polynomial approximation of the nonlinear voltage-dependent magnesium block of NMDAR current. We study the dynamics of the resulting system for two example cases of excitatory cortical neurons and inhibitory striatal neurons. Bifurcation diagrams are presented comparing the different dynamical regimes as compared to the case of linear NMDAR currents, along with sample comparison simulation time series demonstrating different possible oscillatory solutions. The omission of the nonlinearity of NMDAR currents results in a shift in the range (and possible disappearance) of the constant high firing rate regime, along with a modulation in the amplitude and frequency power spectrum of oscillations. Moreover, nonlinear NMDAR action is seen to be state-dependent and can have opposite effects depending on the type of neurons involved and the level of input firing rate received. The presented model can serve as a computationally efficient building block in whole brain network models for investigating the differential modulation of different types of synapses under neuromodulatory influence or receptor specific malfunction.

3.
Natl Sci Rev ; 11(5): nwae144, 2024 May.
Artículo en Inglés | MEDLINE | ID: mdl-38742233
4.
Natl Sci Rev ; 11(5): nwae079, 2024 May.
Artículo en Inglés | MEDLINE | ID: mdl-38698901

RESUMEN

Virtual brain twins are personalized, generative and adaptive brain models based on data from an individual's brain for scientific and clinical use. After a description of the key elements of virtual brain twins, we present the standard model for personalized whole-brain network models. The personalization is accomplished using a subject's brain imaging data by three means: (1) assemble cortical and subcortical areas in the subject-specific brain space; (2) directly map connectivity into the brain models, which can be generalized to other parameters; and (3) estimate relevant parameters through model inversion, typically using probabilistic machine learning. We present the use of personalized whole-brain network models in healthy ageing and five clinical diseases: epilepsy, Alzheimer's disease, multiple sclerosis, Parkinson's disease and psychiatric disorders. Specifically, we introduce spatial masks for relevant parameters and demonstrate their use based on the physiological and pathophysiological hypotheses. Finally, we pinpoint the key challenges and future directions.

5.
Clin Neurophysiol ; 163: 14-21, 2024 Jul.
Artículo en Inglés | MEDLINE | ID: mdl-38663099

RESUMEN

OBJECTIVE: To test the hypothesis that patients affected by Amyotrophic Lateral Sclerosis (ALS) show an altered spatio-temporal spreading of neuronal avalanches in the brain, and that this may related to the clinical picture. METHODS: We obtained the source-reconstructed magnetoencephalography (MEG) signals from thirty-six ALS patients and forty-two healthy controls. Then, we used the construct of the avalanche transition matrix (ATM) and the corresponding network parameter nodal strength to quantify the changes in each region, since this parameter provides key information about which brain regions are mostly involved in the spreading avalanches. RESULTS: ALS patients presented higher values of the nodal strength in both cortical and sub-cortical brain areas. This parameter correlated directly with disease duration. CONCLUSIONS: In this work, we provide a deeper characterization of neuronal avalanches propagation in ALS, describing their spatio-temporal trajectories and identifying the brain regions most likely to be involved in the process. This makes it possible to recognize the brain areas that take part in the pathogenic mechanisms of ALS. Furthermore, the nodal strength of the involved regions correlates directly with disease duration. SIGNIFICANCE: Our results corroborate the clinical relevance of aperiodic, fast large-scale brain activity as a biomarker of microscopic changes induced by neurophysiological processes.


Asunto(s)
Esclerosis Amiotrófica Lateral , Magnetoencefalografía , Humanos , Esclerosis Amiotrófica Lateral/fisiopatología , Esclerosis Amiotrófica Lateral/diagnóstico , Femenino , Masculino , Persona de Mediana Edad , Magnetoencefalografía/métodos , Anciano , Adulto , Ondas Encefálicas/fisiología , Encéfalo/fisiopatología
6.
Brain Commun ; 6(2): fcae112, 2024.
Artículo en Inglés | MEDLINE | ID: mdl-38585670

RESUMEN

Large-scale brain activity has long been investigated under the erroneous assumption of stationarity. Nowadays, we know that resting-state functional connectivity is characterized by aperiodic, scale-free bursts of activity (i.e. neuronal avalanches) that intermittently recruit different brain regions. These different patterns of activity represent a measure of brain flexibility, whose reduction has been found to predict clinical impairment in multiple neurodegenerative diseases such as Parkinson's disease, amyotrophic lateral sclerosis and Alzheimer's disease. Brain flexibility has been recently found increased in multiple sclerosis, but its relationship with clinical disability remains elusive. Also, potential differences in brain dynamics according to the multiple sclerosis clinical phenotypes remain unexplored so far. We performed a brain dynamics study quantifying brain flexibility utilizing the 'functional repertoire' (i.e. the number of configurations of active brain areas) through source reconstruction of magnetoencephalography signals in a cohort of 25 multiple sclerosis patients (10 relapsing-remitting multiple sclerosis and 15 secondary progressive multiple sclerosis) and 25 healthy controls. Multiple sclerosis patients showed a greater number of unique reconfigurations at fast time scales as compared with healthy controls. This difference was mainly driven by the relapsing-remitting multiple sclerosis phenotype, whereas no significant differences in brain dynamics were found between secondary progressive multiple sclerosis and healthy controls. Brain flexibility also showed a different predictive power on clinical disability according to the multiple sclerosis type. For the first time, we investigated brain dynamics in multiple sclerosis patients through high temporal resolution techniques, unveiling differences in brain flexibility according to the multiple sclerosis phenotype and its relationship with clinical disability.

7.
medRxiv ; 2024 Mar 26.
Artículo en Inglés | MEDLINE | ID: mdl-38585976

RESUMEN

The conventional intracarotid amobarbital (Wada) test has been used to assess memory function in patients being considered for temporal lobe epilepsy (TLE) surgery. Minimally invasive approaches that target the medial temporal lobe (MTL) and spare neocortex are increasingly used, but a knowledge gap remains in how to assess memory and language risk from these procedures. We retrospectively compared results of two versions of the Wada test, the intracarotid artery (ICA-Wada) and posterior cerebral artery (PCA-Wada) approaches, with respect to predicting subsequent memory and language outcomes, particularly after stereotactic laser amygdalohippocampotomy (SLAH). We included all patients being considered for SLAH who underwent both ICA-Wada and PCA-Wada at a single institution. Memory and confrontation naming assessments were conducted using standardized neuropsychological tests to assess pre- to post-surgical changes in cognitive performance. Of 13 patients who initially failed the ICA-Wada, only one patient subsequently failed the PCA-Wada (p=0.003, two-sided binomial test with p 0 =0.5) demonstrating that these tests assess different brain regions or networks. PCA-Wada had a high negative predictive value for the safety of SLAH, compared to ICA-Wada, as none of the patients who underwent SLAH after passing the PCA-Wada experienced catastrophic memory decline (0 of 9 subjects, p <.004, two-sided binomial test with p 0 =0.5), and all experienced a good cognitive outcome. In contrast, the single patient who received a left anterior temporal lobectomy after failed ICA- and passed PCA-Wada experienced a persistent, near catastrophic memory decline. On confrontation naming, few patients exhibited disturbance during the PCA-Wada. Following surgery, SLAH patients showed no naming decline, while open resection patients, whose surgeries all included ipsilateral temporal lobe neocortex, experienced significant naming difficulties (Fisher's exact test, p <.05). These findings demonstrate that (1) failing the ICA-Wada falsely predicts memory decline following SLAH, (2) PCA-Wada better predicts good memory outcomes of SLAH for MTLE, and (3) the MTL brain structures affected by both PCA-Wada and SLAH are not directly involved in language processing.

8.
PLoS Comput Biol ; 20(3): e1011903, 2024 Mar.
Artículo en Inglés | MEDLINE | ID: mdl-38446814

RESUMEN

The Epileptor is a phenomenological model for seizure activity that is used in a personalized large-scale brain modeling framework, the Virtual Epileptic Patient, with the aim of improving surgery outcomes for drug-resistant epileptic patients. Transitions between interictal and ictal states are modeled as bifurcations, enabling the definition of seizure classes in terms of onset/offset bifurcations. This establishes a taxonomy of seizures grounded in their essential underlying dynamics and the Epileptor replicates the activity of the most common class, as observed in patients with focal epilepsy, which is characterized by square-wave bursting properties. The Epileptor also encodes an additional mechanism to account for interictal spikes and spike and wave discharges. Here we use insights from a more generic model for square-wave bursting, based on the Unfolding Theory approach, to guide the bifurcation analysis of the Epileptor and gain a deeper understanding of the model and the role of its parameters. We show how the Epileptor's parameters can be modified to produce activities for other seizures classes of the taxonomy, as observed in patients, so that the large-scale brain models could be further personalized. Some of these classes have already been described in the literature in the Epileptor, others, predicted by the generic model, are new. Finally, we unveil how the interaction with the additional mechanism for spike and wave discharges alters the bifurcation structure of the main burster.


Asunto(s)
Epilepsias Parciales , Epilepsia , Humanos , Convulsiones , Electroencefalografía
9.
Front Neuroinform ; 18: 1156683, 2024.
Artículo en Inglés | MEDLINE | ID: mdl-38410682

RESUMEN

Integration of information across heterogeneous sources creates added scientific value. Interoperability of data, tools and models is, however, difficult to accomplish across spatial and temporal scales. Here we introduce the toolbox Parallel Co-Simulation, which enables the interoperation of simulators operating at different scales. We provide a software science co-design pattern and illustrate its functioning along a neuroscience example, in which individual regions of interest are simulated on the cellular level allowing us to study detailed mechanisms, while the remaining network is efficiently simulated on the population level. A workflow is illustrated for the use case of The Virtual Brain and NEST, in which the CA1 region of the cellular-level hippocampus of the mouse is embedded into a full brain network involving micro and macro electrode recordings. This new tool allows integrating knowledge across scales in the same simulation framework and validating them against multiscale experiments, thereby largely widening the explanatory power of computational models.

10.
iScience ; 27(1): 108734, 2024 Jan 19.
Artículo en Inglés | MEDLINE | ID: mdl-38226174

RESUMEN

Large-scale interactions among multiple brain regions manifest as bursts of activations called neuronal avalanches, which reconfigure according to the task at hand and, hence, might constitute natural candidates to design brain-computer interfaces (BCIs). To test this hypothesis, we used source-reconstructed magneto/electroencephalography during resting state and a motor imagery task performed within a BCI protocol. To track the probability that an avalanche would spread across any two regions, we built an avalanche transition matrix (ATM) and demonstrated that the edges whose transition probabilities significantly differed between conditions hinged selectively on premotor regions in all subjects. Furthermore, we showed that the topology of the ATMs allows task-decoding above the current gold standard. Hence, our results suggest that neuronal avalanches might capture interpretable differences between tasks that can be used to inform brain-computer interfaces.

11.
Netw Neurosci ; 7(4): 1420-1451, 2023.
Artículo en Inglés | MEDLINE | ID: mdl-38144688

RESUMEN

Spontaneous activity during the resting state, tracked by BOLD fMRI imaging, or shortly rsfMRI, gives rise to brain-wide dynamic patterns of interregional correlations, whose structured flexibility relates to cognitive performance. Here, we analyze resting-state dynamic functional connectivity (dFC) in a cohort of older adults, including amnesic mild cognitive impairment (aMCI, N = 34) and Alzheimer's disease (AD, N = 13) patients, as well as normal control (NC, N = 16) and cognitively "supernormal" controls (SNC, N = 10) subjects. Using complementary state-based and state-free approaches, we find that resting-state fluctuations of different functional links are not independent but are constrained by high-order correlations between triplets or quadruplets of functionally connected regions. When contrasting patients with healthy subjects, we find that dFC between cingulate and other limbic regions is increasingly bursty and intermittent when ranking the four groups from SNC to NC, aMCI and AD. Furthermore, regions affected at early stages of AD pathology are less involved in higher order interactions in patient than in control groups, while pairwise interactions are not significantly reduced. Our analyses thus suggest that the spatiotemporal complexity of dFC organization is precociously degraded in AD and provides a richer window into the underlying neurobiology than time-averaged FC connections.

12.
Neuroimage ; 283: 120403, 2023 Dec 01.
Artículo en Inglés | MEDLINE | ID: mdl-37865260

RESUMEN

The mechanisms of cognitive decline and its variability during healthy aging are not fully understood, but have been associated with reorganization of white matter tracts and functional brain networks. Here, we built a brain network modeling framework to infer the causal link between structural connectivity and functional architecture and the consequent cognitive decline in aging. By applying in-silico interhemispheric degradation of structural connectivity, we reproduced the process of functional dedifferentiation during aging. Thereby, we found the global modulation of brain dynamics by structural connectivity to increase with age, which was steeper in older adults with poor cognitive performance. We validated our causal hypothesis via a deep-learning Bayesian approach. Our results might be the first mechanistic demonstration of dedifferentiation during aging leading to cognitive decline.


Asunto(s)
Envejecimiento Saludable , Sustancia Blanca , Humanos , Anciano , Teorema de Bayes , Encéfalo , Envejecimiento/psicología , Imagen por Resonancia Magnética
13.
J Comput Neurosci ; 51(4): 445-462, 2023 11.
Artículo en Inglés | MEDLINE | ID: mdl-37667137

RESUMEN

Electrical stimulation is an increasingly popular method to terminate epileptic seizures, yet it is not always successful. A potential reason for inconsistent efficacy is that stimuli are applied empirically without considering the underlying dynamical properties of a given seizure. We use a computational model of seizure dynamics to show that different bursting classes have disparate responses to aborting stimulation. This model was previously validated in a large set of human seizures and led to a description of the Taxonomy of Seizure Dynamics and the dynamotype, which is the clinical analog of the bursting class. In the model, the stimulation is realized as an applied input, which successfully aborts the burst when it forces the system from a bursting state to a quiescent state. This transition requires bistability, which is not present in all bursters. We examine how topological and geometric differences in the bistable state affect the probability of termination as the burster progresses from onset to offset. We find that the most significant determining factors are the burster class (dynamotype) and whether the burster has a DC (baseline) shift. Bursters with a baseline shift are far more likely to be terminated due to the necessary structure of their state space. Furthermore, we observe that the probability of termination varies throughout the burster's duration, is often dependent on the phase when it was applied, and is highly correlated to dynamotype. Our model provides a method to predict the optimal method of termination for each dynamotype. These results lead to the prediction that optimization of ictal aborting stimulation should account for seizure dynamotype, the presence of a DC shift, and the timing of the stimulation.


Asunto(s)
Epilepsia , Modelos Neurológicos , Humanos , Convulsiones , Epilepsia/terapia , Electroencefalografía/métodos
14.
JAMA Psychiatry ; 80(10): 1066-1074, 2023 10 01.
Artículo en Inglés | MEDLINE | ID: mdl-37610741

RESUMEN

Importance: Climate change, pollution, urbanization, socioeconomic inequality, and psychosocial effects of the COVID-19 pandemic have caused massive changes in environmental conditions that affect brain health during the life span, both on a population level as well as on the level of the individual. How these environmental factors influence the brain, behavior, and mental illness is not well known. Observations: A research strategy enabling population neuroscience to contribute to identify brain mechanisms underlying environment-related mental illness by leveraging innovative enrichment tools for data federation, geospatial observation, climate and pollution measures, digital health, and novel data integration techniques is described. This strategy can inform innovative treatments that target causal cognitive and molecular mechanisms of mental illness related to the environment. An example is presented of the environMENTAL Project that is leveraging federated cohort data of over 1.5 million European citizens and patients enriched with deep phenotyping data from large-scale behavioral neuroimaging cohorts to identify brain mechanisms related to environmental adversity underlying symptoms of depression, anxiety, stress, and substance misuse. Conclusions and Relevance: This research will lead to the development of objective biomarkers and evidence-based interventions that will significantly improve outcomes of environment-related mental illness.


Asunto(s)
COVID-19 , Salud Mental , Humanos , COVID-19/epidemiología , Pandemias , Trastornos de Ansiedad , Ansiedad
15.
Front Aging Neurosci ; 15: 1204134, 2023.
Artículo en Inglés | MEDLINE | ID: mdl-37577354

RESUMEN

Introduction: Neural circuit alterations lay at the core of brain physiopathology, and yet are hard to unveil in living subjects. The Virtual Brain (TVB) modeling, by exploiting structural and functional magnetic resonance imaging (MRI), yields mesoscopic parameters of connectivity and synaptic transmission. Methods: We used TVB to simulate brain networks, which are key for human brain function, in Alzheimer's disease (AD) and frontotemporal dementia (FTD) patients, whose connectivity and synaptic parameters remain largely unknown; we then compared them to healthy controls, to reveal novel in vivo pathological hallmarks. Results: The pattern of simulated parameter differed between AD and FTD, shedding light on disease-specific alterations in brain networks. Individual subjects displayed subtle differences in network parameter patterns that significantly correlated with their individual neuropsychological, clinical, and pharmacological profiles. Discussion: These TVB simulations, by informing about a new personalized set of networks parameters, open new perspectives for understanding dementias mechanisms and design personalized therapeutic approaches.

16.
Neuroimage ; 277: 120260, 2023 08 15.
Artículo en Inglés | MEDLINE | ID: mdl-37392807

RESUMEN

Subject differentiation bears the possibility to individualize brain analyses. However, the nature of the processes generating subject-specific features remains unknown. Most of the current literature uses techniques that assume stationarity (e.g., Pearson's correlation), which might fail to capture the non-linear nature of brain activity. We hypothesize that non-linear perturbations (defined as neuronal avalanches in the context of critical dynamics) spread across the brain and carry subject-specific information, contributing the most to differentiability. To test this hypothesis, we compute the avalanche transition matrix (ATM) from source-reconstructed magnetoencephalographic data, as to characterize subject-specific fast dynamics. We perform differentiability analysis based on the ATMs, and compare the performance to that obtained using Pearson's correlation (which assumes stationarity). We demonstrate that selecting the moments and places where neuronal avalanches spread improves differentiation (P < 0.0001, permutation testing), despite the fact that most of the data (i.e., the linear part) are discarded. Our results show that the non-linear part of the brain signals carries most of the subject-specific information, thereby clarifying the nature of the processes that underlie individual differentiation. Borrowing from statistical mechanics, we provide a principled way to link emergent large-scale personalized activations to non-observable, microscopic processes.


Asunto(s)
Encéfalo , Modelos Neurológicos , Humanos , Encéfalo/fisiología , Magnetoencefalografía , Mapeo Encefálico , Neuronas/fisiología
17.
Neuroimage Clin ; 39: 103464, 2023.
Artículo en Inglés | MEDLINE | ID: mdl-37399676

RESUMEN

BACKGROUND: Brain connectome fingerprinting is progressively gaining ground in the field of brain network analysis. It represents a valid approach in assessing the subject-specific connectivity and, according to recent studies, in predicting clinical impairment in some neurodegenerative diseases. Nevertheless, its performance, and clinical utility, in the Multiple Sclerosis (MS) field has not yet been investigated. METHODS: We conducted the Clinical Connectome Fingerprint (CCF) analysis on source-reconstructed magnetoencephalography signals in a cohort of 50 subjects: twenty-five MS patients and twenty-five healthy controls. RESULTS: All the parameters of identifiability, in the alpha band, were reduced in patients as compared to controls. These results implied a lower similarity between functional connectomes (FCs) of the same patient and a reduced homogeneity among FCs in the MS group. We also demonstrated that in MS patients, reduced identifiability was able to predict, fatigue level (assessed by the Fatigue Severity Scale). CONCLUSION: These results confirm the clinical usefulness of the CCF in both identifying MS patients and predicting clinical impairment. We hope that the present study provides future prospects for treatment personalization on the basis of individual brain connectome.


Asunto(s)
Conectoma , Esclerosis Múltiple , Humanos , Conectoma/métodos , Esclerosis Múltiple/diagnóstico por imagen , Imagen por Resonancia Magnética/métodos , Encéfalo/diagnóstico por imagen , Fatiga/diagnóstico por imagen , Fatiga/etiología
18.
Cell Rep ; 42(5): 112491, 2023 05 30.
Artículo en Inglés | MEDLINE | ID: mdl-37171963

RESUMEN

Brain states are frequently represented using a unidimensional scale measuring the richness of subjective experience (level of consciousness). This description assumes a mapping between the high-dimensional space of whole-brain configurations and the trajectories of brain states associated with changes in consciousness, yet this mapping and its properties remain unclear. We combine whole-brain modeling, data augmentation, and deep learning for dimensionality reduction to determine a mapping representing states of consciousness in a low-dimensional space, where distances parallel similarities between states. An orderly trajectory from wakefulness to patients with brain injury is revealed in a latent space whose coordinates represent metrics related to functional modularity and structure-function coupling, increasing alongside loss of consciousness. Finally, we investigate the effects of model perturbations, providing geometrical interpretation for the stability and reversibility of states. We conclude that conscious awareness depends on functional patterns encoded as a low-dimensional trajectory within the vast space of brain configurations.


Asunto(s)
Lesiones Encefálicas , Estado de Conciencia , Humanos , Encéfalo , Vigilia , Vías Nerviosas , Imagen por Resonancia Magnética , Mapeo Encefálico
19.
Epilepsy Behav ; 142: 109175, 2023 05.
Artículo en Inglés | MEDLINE | ID: mdl-37003103

RESUMEN

How status epilepticus (SE) is generated and propagates in the brain is not known. As for seizures, a patient-specific approach is necessary, and the analysis should be performed at the whole brain level. Personalized brain models can be used to study seizure genesis and propagation at the whole brain scale in The Virtual Brain (TVB), using the Epileptor mathematical construct. Building on the fact that SE is part of the repertoire of activities that the Epileptor can generate, we present the first attempt to model SE at the whole brain scale in TVB, using data from a patient who experienced SE during presurgical evaluation. Simulations reproduced the patterns found with SEEG recordings. We find that if, as expected, the pattern of SE propagation correlates with the properties of the patient's structural connectome, SE propagation also depends upon the global state of the network, i.e., that SE propagation is an emergent property. We conclude that individual brain virtualization can be used to study SE genesis and propagation. This type of theoretical approach may be used to design novel interventional approaches to stop SE. This paper was presented at the 8th London-Innsbruck Colloquium on Status Epilepticus and Acute Seizures held in September 2022.


Asunto(s)
Conectoma , Estado Epiléptico , Humanos , Estado Epiléptico/diagnóstico por imagen , Convulsiones , Encéfalo/diagnóstico por imagen , Londres
20.
Neurobiol Dis ; 182: 106131, 2023 06 15.
Artículo en Inglés | MEDLINE | ID: mdl-37086755

RESUMEN

Epilepsy is a complex disease that requires various approaches for its study. This short review discusses the contribution of theoretical and computational models. The review presents theoretical frameworks that underlie the understanding of certain seizure properties and their classification based on their dynamical properties at the onset and offset of seizures. Dynamical system tools are valuable resources in the study of seizures. These tools can provide insights into seizure mechanisms and offer a framework for their classification, by analyzing the complex, dynamic behavior of seizures. Additionally, computational models have high potential for clinical applications, as they can be used to develop more accurate diagnostic and personalized medicine tools. We discuss various modeling approaches that span different scales and levels, while also questioning the neurocentric view, emphasizing the importance of considering glial cells. Finally, we explore the epistemic value provided by this type of approach.


Asunto(s)
Epilepsia , Modelos Neurológicos , Humanos , Convulsiones , Biofisica
SELECCIÓN DE REFERENCIAS
DETALLE DE LA BÚSQUEDA
...