Biology subjects

Valiante, T.

Publications and source records attributed to Valiante, T..

10 recordsLinked to original sources

bioRxiv2022

OpenMEA: Open-Source Microelectrode Array Platform for Bioelectronic Interfacing

Bioelectronic interfaces have the potential to revolutionize the treatment of medical disorders and augment physiology. Implantable devices such as pacemakers and deep brain stimulators have already been deployed to control activity in diseases including Parkinsons disease and epilepsy. These devices typically operate by delivering electrical stimulation at pre-programmed intervals (known as open-loop stimulation). Recent advances in machine learning and low-power integrated circuits have led to the emergence of personalized medical devices that monitor the users state and stimulate in response to measured biological activity (known as closed-loop stimulation). There are two key questions that require fundamental research to achieve breakthroughs in personalized devices: 1) What biomarkers and algorithms are best suited to detecting biological states (e.g. seizures in epilepsy)? and 2) What types of electrical stimuli are optimal for controlling these states? The answer to these questions can be explored in vitro using multielectrode array (MEA) systems that interface with biological tissue with reduced experimental complexity, better reproducibility, and fewer confounding variables present in whole organisms. However, existing MEA systems have functional limitations and closed-source designs that prevent researchers from developing improvements. This paper introduces OpenMEA, an open-source platform for closed-loop bioelectronics research. OpenMEA includes designs for the components necessary to build a benchtop in vitro laboratory, including electrophysiological recording and stimulation electronics, a microfluidic perfusion system, and physical designs for multielectrode arrays. The system is demonstrated with the electrical recording and stimulation of epileptogenic human and rodent brain slices. The aim of OpenMEA is to democratize bioelectronic research tools to accelerate the deployment of devices for the treatment of disorders and beyond.

bioengineering↗

bioRxiv2022

Inter-regional delays fluctuate in the human cerebral cortex

The flow of information between cortical regions depends on the excitability at each site, which is reflected in fluctuating field potentials. It remains uncertain how global changes in field potentials affect the latency and strength of cortico-cortical couplings. Therefore, we measured changes in oscillations and inter-regional couplings by recording intracranially from the human cerebral cortex. As participants listened to an auditory narrative, global increases in low-frequency (4-14 Hz) power were associated with stronger and more delayed inter-regional couplings. Conversely, increases in broadband high-frequency power were associated with weaker coupling and zero lag. In network oscillator models, these changes in cortico-cortical latency can be generated by varying the effective influence of inter-regional projections relative to intra-regional dynamics. Altogether, low-frequency oscillations appear to modulate information flow across the human cerebral cortex, as they covary with the timing of peak excitability between regions, and this process may be regulated by nonspecific ascending projections.

neuroscience↗

bioRxiv2021

Beamforming seizures from the temporal lobe

BackgroundSurgical treatment of drug-resistant temporal lobe epilepsy (TLE) depends on proper identification of the seizure onset zone (SOZ), and differentiation of mesial, temporolimbic seizure onsets from temporal neocortical seizure onsets. Non-invasive source imaging using electroencephalography (EEG) and magnetoencephalography (MEG) can provide accurate information on interictal spike localization; however, EEG and MEG have low sensitivity for epileptiform activity restricted to deep temporolimbic structures. Moreover, in mesial temporal lobe epilepsy (MTLE), interictal spikes frequently arise in neocortical foci distant from the SOZ, rendering interictal spike localization potentially misleading for presurgical planning. MethodsIn this study, we used two different beamformer techniques applied to the MEG signal of ictal events acquired during EEG-MEG recordings in six patients with TLE (three neocortical, three MTLE). The ictal source localization results were compared to the patients ground truth SOZ localizations determined from intracranial EEG and/or clinical, neuroimaging and postsurgical outcome evidence. ResultsBeamformer analysis proved to be highly accurate in all cases and able to reliably identify focal seizure onsets localized to mesial, temporolimbic structures. In three patients, interictal spikes were either absent, too complex for inverse dipole modeling, or localized to anterolateral temporal neocortex distant to a mesial temporal SOZ. ConclusionsThis report demonstrates the suitability of MEG beamformer analysis of ictal events in TLE, which can supersede or complement the traditional analysis of interictal spikes. The method outlined is applicable to any type of epileptiform event, greatly expanding the information value of MEG and broadening its utility for presurgical recording in epilepsy.

neuroscience↗

bioRxiv2021

Molecular atlas of the human brain vasculature at the single-cell level

A broad range of brain pathologies critically relies on the vasculature, and cerebrovascular disease is a leading cause of death worldwide. However, the cellular and molecular architecture of the human brain vasculature remains poorly understood. Here, we performed single-cell RNA sequencing of 599,215 freshly isolated endothelial, perivascular and other tissue-derived cells from 47 fetuses and adult patients to construct a molecular atlas of the developing fetal, adult control and diseased human brain vasculature. We uncover extensive molecular heterogeneity of healthy fetal and adult human brains and across eight vascular-dependent CNS pathologies including brain tumors and brain vascular malformations. We identify alteration of arteriovenous differentiation and reactivated fetal as well as conserved dysregulated pathways in the diseased vasculature. Pathological endothelial cells display a loss of CNS-specific properties and reveal an upregulation of MHC class II molecules, indicating atypical features of CNS endothelial cells. Cell-cell interaction analyses predict numerous endothelial-to-perivascular cell ligand-receptor crosstalk including immune-related and angiogenic pathways, thereby unraveling a central role for the endothelium within brain neurovascular unit signaling networks. Our single-cell brain atlas provides insight into the molecular architecture and heterogeneity of the developing, adult/control and diseased human brain vasculature and serves as a powerful reference for future studies.

neuroscience↗

bioRxiv2021

Long range, high-gamma phase coherence in the human brain during overt and covert speech

Using intracranial electroencephalography (iEEG) in patients undergoing diagnostic workup for epilepsy, we show that during reading, phase coherent high-gamma activity emerges between spatially distant regions of proven involvement in vocalization. Using the novel metric of "phase-dependent power-causal correlations", causal interactions were shown to be maximal at high-gamma frequencies, and displayed stronger correlations to power fluctuations near the optimal time delay, with this delay potentially accounted for by axonal conduction. We conclude that high-gamma activity may represents propagated, directed, large-scale integration between task related regions of the human brain.

neuroscience↗

bioRxiv2020

Unsupervised seizure localisation with attention-based graph neural networks

In this paper, we introduce a machine learning methodology for localising the seizure onset zone in subjects with epilepsy. We represent brain states as functional networks obtained from intracranial electroen-cephalography recordings, using correlation and the phase locking value to quantify the coupling between different brain areas. Our method is based on graph neural networks (GNNs) and the attention mechanism, two of the most significant advances in artificial intelligence in recent years. Specifically, we train a GNN to distinguish between functional networks associated with interictal and ictal phases. The GNN is equipped with an attention-based layer that automatically learns to identify those regions of the brain (associated with individual electrodes) that are most important for a correct classification. The localisation of these regions does not require any prior information regarding the seizure onset zone. We show that the regions of interest identified by the GNN strongly correlate with the localisation of the seizure onset zone reported by electroencephalographers. We report results both for human patients and for simulators of brain activity. We also show that our GNN exhibits uncertainty on those patients for which the clinical localisation was unsuccessful, highlighting the robustness of the proposed approach.

neuroscience↗

bioRxiv2020

The human brain vasculature shows a distinct expression pattern of SARS-CoV-2 entry factors

A large number of hospitalized COVID-19 patients show neurological symptoms such as ischemic- and hemorrhagic stroke as well as encephalitis, and SARS-CoV-2 can directly infect endothelial cells leading to endotheliitis across multiple vascular beds. These findings suggest an involvement of the brain- and peripheral vasculature in COVID-19, but the underlying molecular mechanisms remain obscure. To understand the potential mechanisms underlying SARS-CoV-2 tropism for brain vasculature, we constructed a molecular atlas of the expression patterns of SARS-CoV-2 viral entry-associated genes (receptors and proteases) and SARS-CoV-2 interaction partners in human (and mouse) adult and fetal brain as well as in multiple non-CNS tissues in single-cell RNA-sequencing data across various datasets. We observed a distinct expression pattern of the cathepsins B (CTSB) and -L (CTSL) - which are able to substitute for the ACE2 co-receptor TMPRSS2 - in the human vasculature with CTSB being mainly expressed in the brain vasculature and CTSL predominantly in the peripheral vasculature, and these observations were confirmed at the protein level in the Human Protein Atlas and using immunofluorescence stainings. This expression pattern of SARS-CoV-2 viral-entry associated proteases and SARS-CoV-2 interaction partners was also present in endothelial cells and microglia in the fetal brain, suggesting a developmentally established SARS-CoV-2 entry machinery in the human vasculature. At both the adult and fetal stages, we detected a distinct pattern of SARS-CoV-2 entry associated genes transcripts in brain vascular endothelial cells and microglia, providing a potential explanation for an inflammatory response in the brain endothelium upon SARS-CoV-2 infection. Moreover, CTSB was co-expressed in adult and fetal brain endothelial cells with genes and pathways involved in innate immunity and inflammation, angiogenesis, blood-brain-barrier permeability, vascular metabolism, and coagulation, providing a potential explanation for the role of brain endothelial cells in clinically observed (neuro)vascular symptoms in COVID-19 patients. Our study serves as a publicly available single-cell atlas of SARS-CoV-2 related entry factors and interaction partners in human and mouse brain endothelial- and perivascular cells, which can be employed for future studies in clinical samples of COVID-19 patients.

neuroscience↗

bioRxiv2019

Modeling implicates inhibitory network bistability as an underpinning of seizure initiation

A plethora of recent experimental literature implicates the abrupt, synchronous activation of GABAergic interneurons in driving the sudden change in brain activity that heralds seizure initiation. However, the mechanisms predisposing an inhibitory network toward sudden coherence specifically during ictogenesis remain unknown. We address this question by comparing simulated inhibitory networks containing control interneurons and networks containing hyper-excitable interneurons modeled to mimic treatment with 4-Aminopyridine (4-AP), an agent commonly used to model seizures in vivo and in vitro. Our in silico study demonstrates that model inhibitory networks with 4-AP interneurons are more prone than their control counterparts to exist in a bistable state in which asynchronously firing networks can abruptly transition into synchrony due to a brief perturbation. We further show that perturbations driving this transition could reasonably arise in vivo based on models of background excitatory synaptic activity in the cortex. Thus, these results propose a mechanism by which an inhibitory network can transition from incoherent to coherent dynamics in a fashion that may precipitate seizure as a downstream effect. Moreover, this mechanism specifically explains why inhibitory networks containing hyper-excitable interneurons are more vulnerable to this state change, and how such networks can undergo this transition without a permanent change in the drive to the system. This, in turn, potentially explains such networks increased vulnerability to seizure initiated by GABAergic activity.\n\nAuthor summaryFor decades, the study of epilepsy has focused on the hypothesis that over-excitation or dis-inhibition of pyramidal neurons underlies the transition from normal brain activity to seizure. However, a variety of recent experimental findings have implicated a sudden synchronous burst of activity amongst inhibitory interneurons in driving this transition. Given the counter-intuitive nature of these findings and the correspondingly novel hypothesis of seizure generation, the articulation of a feasible mechanism of action underlying this dynamic is of paramount importance for this theorys viability. Here, we use computational techniques, particularly the concept of bistability in the context of dynamical systems, to propose a mechanism for the necessary first step in such a process: the sudden synchronization of a network of inhibitory interneurons. This is the first detailed proposal of a computational mechanism explaining any aspect of this hypothesis of which we are aware. By articulating a mechanism that not only underlies this transition, but does so in a fashion explaining why ictogenic networks might be more prone to this behavior, we provide critical support for this novel hypothesis of seizure generation and potential insight into the larger question of why individuals with epilepsy are particularly vulnerable to seizure.

neuroscience↗

bioRxiv2018

Cluster Tendency Assessment in Neuronal Spike Data

Sorting spikes from extracellular recording into clusters associated with distinct single units (putative neurons) is a fundamental step in analyzing neuronal populations. Such spike sorting is intrinsically unsupervised, as the number of neurons are not known a priori. Therefor, any spike sorting is an unsupervised learning problem that requires either of the two approaches: specification of a fixed value c for the number of clusters to seek, or generation of candidate partitions for several possible values of c, followed by selection of a best candidate based on various post-clustering validation criteria. In this paper, we investigate the first approach and evaluate the utility of several methods for providing lower dimensional visualization of the cluster structure and on subsequent spike clustering. We also introduce a visualization technique called improved visual assessment of cluster tendency (iVAT) to estimate possible cluster structures in data without the need for dimensionality reduction. Experimental results are conducted on two datasets with ground truth labels. In data with a relatively small number of clusters, iVAT is beneficial in estimating the number of clusters to inform the initialization of clustering algorithms. With larger numbers of clusters, iVAT gives a useful estimate of the coarse cluster structure but sometimes fails to indicate the presumptive number of clusters. We show that noise associated with recording extracellular neuronal potentials can disrupt computational clustering schemes, highlighting the benefit of probabilistic clustering models. Our results show that t-Distributed Stochastic Neighbor Embedding (t-SNE) provides representations of the data that yield more accurate visualization of potential cluster structure to inform the clustering stage. Moreover, The clusters obtained using t-SNE features were more reliable than the clusters obtained using the other methods, which indicates that t-SNE can potentially be used for both visualization and to extract features to be used by any clustering algorithm.

neuroscience↗

bioRxiv2017

Eyes closed or Eyes open? Exploring the alpha desynchronization hypothesis in resting state functional connectivity networks with intracranial EEG

This paper addresses a fundamental question, are eyes closed and eyes open resting states equivalent baseline conditions, or do they have consistently different electrophysiological signatures? We compare the functional connectivity patterns in an eyes closed resting state with an eyes open resting state, and show that functional connectivity in the alpha band decreases in the eyes open condition compared to eyes closed. This \"alpha desynchronization \" or reduction in the number of connections from eyes closed to eyes open, is here, for the first time, studied with intracranial recordings. We provide two calculations of the wiring cost, local and mesoscopic, defined in terms of the distance between the electrodes and the likelihood that they are functionally connected. We find that, in agreement with the \"alpha desynchronization\" hypothesis, the local wiring cost decreases going from eyes closed to eyes open. However, when the wiring cost calculation takes into account the connectivity pattern, the wiring cost variation from eyes closed to eyes open is not as consistent and shows regional specificity. The wiring cost measure defined here, provides a new avenue for understanding the electrophysiology of resting state.

neuroscience↗