A live mammalian cells electroporation array for on-chip immunofluorescence

The detection of intracellular proteins in vitro is commonly realized with immunofluorescence techniques, through which antibodies or markers are delivered into fixed cells and recognize specific proteins. Many innovative techniques, however, avoid cells fixation by chemical compounds and, among the others, electroporation is widely used. Here we demonstrate that in situ electroporation on thin film SiO2 capacitive microelectrodes can be realized with high efficiency to deliver fluorescent markers and antibodies into mammalian cell lines and primary neuronal cells to detect intracellular proteins, like actin. The results presented in this work open the way to the use of this technique for the detection of potentially any target protein, even through subsequent electroporations.

Interfacing Biology and Electronics with Memristive Materials

Memristive technologies promise to have a large impact on modern electronics, particularly in the areas of reconfigurable computing and artificial intelligence (AI) hardware. Meanwhile, the evolution of memristive materials alongside the technological progress is opening application perspectives also in the biomedical field, particularly for implantable and lab-on-a-chip devices where advanced sensing technologies generate a large amount of data. Memristive devices are emerging as bioelectronic links merging biosensing with computation, acting as physical processors of analog signals or in the framework of advanced digital computing architectures. Recent developments in the processing of electrical neural signals, as well as on transduction and processing of chemical biomarkers of neural and endocrine functions, are reviewed. It is concluded with a critical perspective on the future applicability of memristive devices as pivotal building blocks in bio-AI fusion concepts and bionic schemes.

Early Detection of Alzheimer's Disease from Cortical and Hippocampal Local Field Potentials using an Ensembled Machine Learning Model

Early diagnosis of Alzheimer's disease (AD) is a very challenging problem and has been attempted through data-driven methods in recent years. However, considering the inherent complexity in decoding higher cognitive functions from spontaneous neuronal signals, these data-driven methods benefit from the incorporation of multimodal data. This work proposes an ensembled machine learning model with explainability (EXML) to detect subtle patterns in cortical and hippocampal local field potential signals (LFPs) that can be considered as a potential marker for AD in the early stage of the disease. The LFPs acquired from healthy and two types of AD animal models (n=10 each) using linear multielectrode probes were endorsed by electrocardiogram and respiration signals for their veracity. Feature sets were generated from LFPs in temporal, spatial and spectral domains and were fed into selected machine-learning models for each domain. Using late fusion, the EXML model achieved an overall accuracy of 99.4%. This provided insights into the amyloid plaque deposition process as early as 3 months of the disease onset by identifying the subtle patterns in the network activities. Lastly, the individual and ensemble models were found to be robust when evaluated by randomly masking channels to mimic the presence of artefacts.

Disentangling the critical signatures of neural activity

The critical brain hypothesis has emerged as an attractive framework to understand neuronal activity, but it is still widely debated. In this work, we analyze data from a multi-electrodes array in the rat’s cortex and we find that power-law neuronal avalanches satisfying the crackling-noise relation coexist with spatial correlations that display typical features of critical systems. In order to shed a light on the underlying mechanisms at the origin of these signatures of criticality, we introduce a paradigmatic framework with a common stochastic modulation and pairwise linear interactions inferred from our data. We show that in such models power-law avalanches that satisfy the crackling-noise relation emerge as a consequence of the extrinsic modulation, whereas scale-free correlations are solely determined by internal interactions. Moreover, this disentangling is fully captured by the mutual information in the system. Finally, we show that analogous power-law avalanches are found in more realistic models of neural activity as well, suggesting that extrinsic modulation might be a broad mechanism for their generation.

Classification of Whisker Deflections From Evoked Responses in the Somatosensory Barrel Cortex With Spiking Neural Networks

Spike-based neuromorphic hardware has great potential for low-energy brain-machine interfaces, leading to a novel paradigm for neuroprosthetics where spiking neurons in silicon read out and control activity of brain circuits. Neuromorphic processors can receive rich information about brain activity from both spikes and local field potentials (LFPs) recorded by implanted neural probes. However, it was unclear whether spiking neural networks (SNNs) implemented on such devices can effectively process that information. Here, we demonstrate that SNNs can be trained to classify whisker deflections of different amplitudes from evoked responses in a single barrel of the rat somatosensory cortex. We show that the classification performance is comparable or even superior to state-of-the-art machine learning approaches. We find that SNNs are rather insensitive to recorded signal type: both multi-unit spiking activity and LFPs yield similar results, where LFPs from cortical layers III and IV seem better suited than those of deep layers. In addition, no hand-crafted features need to be extracted from the data—multi-unit activity can directly be fed into these networks and a simple event-encoding of LFPs is sufficient for good performance. Furthermore, we find that the performance of SNNs is insensitive to the network state—their performance is similar during UP and DOWN states.

Increased fMRI connectivity upon chemogenetic inhibition of the mouse prefrontal cortex

While shaped and constrained by axonal connections, fMRI-based functional connectivity reorganizes in response to varying interareal input or pathological perturbations. However, the causal contribution of regional brain activity to whole-brain fMRI network organization remains unclear. Here we combine neural manipulations, resting-state fMRI and in vivo electrophysiology to probe how inactivation of a cortical node causally affects brain-wide fMRI coupling in the mouse. We find that chronic inhibition of the medial prefrontal cortex (PFC) via overexpression of a potassium channel increases fMRI connectivity between the inhibited area and its direct thalamo-cortical targets. Acute chemogenetic inhibition of the PFC produces analogous patterns of fMRI overconnectivity. Using in vivo electrophysiology, we find that chemogenetic inhibition of the PFC enhances low frequency (0.1–4 Hz) oscillatory power via suppression of neural firing not phase-locked to slow rhythms, resulting in increased slow and δ band coherence between areas that exhibit fMRI overconnectivity. These results provide causal evidence that cortical inactivation can counterintuitively increase fMRI connectivity via enhanced, less-localized slow oscillatory processes.

Pathological perturbation affects whole brain network activity. Here the authors show in mice that cortical inactivation unexpectedly results in increased fMRI connectivity between the manipulated regions and its direct axonal targets.

Accelerated Aging Characterizes the Early Stage of Alzheimer's Disease

For Alzheimer's disease (AD), aging is the main risk factor, but whether cognitive impairments due to aging resemble early AD deficits is not yet defined. When working with mouse models of AD, the situation is just as complicated, because only a few studies track the progression of the disease at different ages, and most ignore how the aging process affects control mice. In this work, we addressed this problem by comparing the aging process of PS2APP (AD) and wild-type (WT) mice at the level of spontaneous brain electrical activity under anesthesia. Using local field potential recordings, obtained with a linear probe that traverses the posterior parietal cortex and the entire hippocampus, we analyzed how multiple electrical parameters are modified by aging in AD and WT mice. With this approach, we highlighted AD specific features that appear in young AD mice prior to plaque deposition or that are delayed at 12 and 16 months of age. Furthermore, we identified aging characteristics present in WT mice but also occurring prematurely in young AD mice. In short, we found that reduction in the relative power of slow oscillations (SO) and Low/High power imbalance are linked to an AD phenotype at its onset. The loss of SO connectivity and cortico-hippocampal coupling between SO and higher frequencies as well as the increase in UP-state and burst durations are found in young AD and old WT mice. We show evidence that the aging process is accelerated by the mutant PS2 itself and discuss such changes in relation to amyloidosis and gliosis.

Simultaneous Two-Photon Voltage or Calcium Imaging and Multi-Channel Local Field Potential Recordings in Barrel Cortex of Awake and Anesthetized Mice

Neuronal population activity, both spontaneous and sensory-evoked, generates propagating waves in cortex. However, high spatiotemporal-resolution mapping of these waves is difficult as calcium imaging, the work horse of current imaging, does not reveal subthreshold activity. Here, we present a platform combining voltage or calcium two-photon imaging with multi-channel local field potential (LFP) recordings in different layers of the barrel cortex from anesthetized and awake head-restrained mice. A chronic cranial window with access port allows injecting a viral vector expressing GCaMP6f or the voltage-sensitive dye (VSD) ANNINE-6plus, as well as entering the brain with a multi-channel neural probe. We present both average spontaneous activity and average evoked signals in response to multi-whisker air-puff stimulations. Time domain analysis shows the dependence of the evoked responses on the cortical layer and on the state of the animal, here separated into anesthetized, awake but resting, and running. The simultaneous data acquisition allows to compare the average membrane depolarization measured with ANNINE-6plus with the amplitude and shape of the LFP recordings. The calcium imaging data connects these data sets to the large existing database of this important second messenger. Interestingly, in the calcium imaging data, we found a few cells which showed a decrease in calcium concentration in response to vibrissa stimulation in awake mice. This system offers a multimodal technique to study the spatiotemporal dynamics of neuronal signals through a 3D architecture in vivo. It will provide novel insights on sensory coding, closing the gap between electrical and optical recordings.

Neuronal Avalanches Across the Rat Somatosensory Barrel Cortex and the Effect of Single Whisker Stimulation

Since its first experimental signatures, the so called "critical brain hypothesis" has been extensively studied. Yet, its actual foundations remain elusive. According to a widely accepted teleological reasoning, the brain would be poised to a critical state to optimize the mapping of the noisy and ever changing real-world inputs, thus suggesting that primary sensory cortical areas should be critical. We investigated whether a single barrel column of the somatosensory cortex of the anesthetized rat displays a critical behavior. Neuronal avalanches were recorded across all cortical layers in terms of both multi-unit activities and population local field potentials, and their behavior during spontaneous activity compared to the one evoked by a controlled single whisker deflection. By applying a maximum likelihood statistical method based on timeseries undersampling to fit the avalanches distributions, we show that neuronal avalanches are power law distributed for both multi-unit activities and local field potentials during spontaneous activity, with exponents that are spread along a scaling line. Instead, after the tactile stimulus, activity switches to a transient across-layers synchronization mode that appears to dominate the cortical representation of the single sensory input.

In situ electroporation of mammalian cells through SiO2 thin film capacitive microelectrodes

Electroporation is a widely used non-viral technique for the delivery of molecules, including nucleic acids, into cells. Recently, electronic microsystems that miniaturize the electroporation machinery have been developed as a new tool for genetic manipulation of cells in vitro, by integrating metal microelectrodes in the culture substrate and enabling electroporation in-situ. We report that non-faradic SiO₂ thin film-insulated microelectrodes can be used for reliable and spatially selective in-situ electroporation of mammalian cells. CHO-K1 and SH-SY5Y cell lines and primary neuronal cultures were electroporated by application of short and low amplitude voltage transients leading to cell electroporation by capacitive currents. We demonstrate reliable delivery of DNA plasmids and exogenous gene expression, accompanied by high spatial selectivity and cell viability, even with differentiated neurons. Finally, we show that SiO₂ thin film-insulated microelectrodes support a double and serial transfection of the targeted cells.

Understanding the Effects of Anesthesia on Cortical Electrophysiological Recordings: A Scoping Review

General anesthesia in animal experiments is an ethical must and is required for all the procedures that are likely to cause more than slight or momentary pain. As anesthetics are known to deeply affect experimental findings, including electrophysiological recordings of brain activity, understanding their mechanism of action is of paramount importance. It is widely recognized that the depth and type of anesthesia introduce significant bias in electrophysiological measurements by affecting the shape of both spontaneous and evoked signals, e.g., modifying their latency and relative amplitude. Therefore, for a given experimental protocol, it is relevant to identify the appropriate anesthetic, to minimize the impact on neuronal circuits and related signals under investigation. This review focuses on the effect of different anesthetics on cortical electrical recordings, examining their molecular mechanisms of action, their influence on neuronal microcircuits and, consequently, their impact on cortical measurements.

Plasticity and Adaptation in Neuromorphic Biohybrid Systems

Neuromorphic systems take inspiration from the principles of biological information processing to form hardware platforms that enable the large-scale implementation of neural networks. The recent years have seen both advances in the theoretical aspects of spiking neural networks for their use in classification and control tasks and a progress in electrophysiological methods that is pushing the frontiers of intelligent neural interfacing and signal processing technologies. At the forefront of these new technologies, artificial and biological neural networks are tightly coupled, offering a novel "biohybrid" experimental framework for engineers and neurophysiologists. Indeed, biohybrid systems can constitute a new class of neuroprostheses opening important perspectives in the treatment of neurological disorders. Moreover, the use of biologically plausible learning rules allows forming an overall fault-tolerant system of co-developing subsystems. To identify opportunities and challenges in neuromorphic biohybrid systems, we discuss the field from the perspectives of neurobiology, computational neuroscience, and neuromorphic engineering.

Memristive synapses connect brain and silicon spiking neurons

Brain function relies on circuits of spiking neurons with synapses playing the key role of merging transmission with memory storage and processing. Electronics has made important advances to emulate neurons and synapses and brain-computer interfacing concepts that interlink brain and brain-inspired devices are beginning to materialise. We report on memristive links between brain and silicon spiking neurons that emulate transmission and plasticity properties of real synapses. A memristor paired with a metal-thin film titanium oxide microelectrode connects a silicon neuron to a neuron of the rat hippocampus. Memristive plasticity accounts for modulation of connection strength, while transmission is mediated by weighted stimuli through the thin film oxide leading to responses that resemble excitatory postsynaptic potentials. The reverse brain-to-silicon link is established through a microelectrode-memristor pair. On these bases, we demonstrate a three-neuron brain-silicon network where memristive synapses undergo long-term potentiation or depression driven by neuronal firing rates.

Dampened Slow Oscillation Connectivity Anticipates Amyloid Deposition in the PS2APP Mouse Model of Alzheimer's Disease

To fight Alzheimer's disease (AD), we should know when, where, and how brain network dysfunctions initiate. In AD mouse models, relevant information can be derived from brain electrical activity. With a multi-site linear probe, we recorded local field potentials simultaneously at the posterior-parietal cortex and hippocampus of wild-type and double transgenic AD mice, under anesthesia. We focused on PS2APP (B6.152H) mice carrying both presenilin-2 (PS2) and amyloid precursor protein (APP) mutations, at three and six months of age, before and after plaque deposition respectively. To highlight defects linked to either the PS2 or APP mutation, we included in the analysis age-matched PS2.30H and APP-Swedish mice, carrying each of the mutations individually. Our study also included PSEN2^-/- mice. At three months, only predeposition B6.152H mice show a reduction in the functional connectivity of slow oscillations (SO) and in the power ratio between SO and delta waves. At six months, plaque-seeding B6.152H mice undergo a worsening of the low/high frequency power imbalance and show a massive loss of cortico-hippocampal phase-amplitude coupling (PAC) between SO and higher frequencies, a feature shared with amyloid-free PS2.30H mice. We conclude that the PS2 mutation is sufficient to impair SO PAC and accelerate network dysfunctions in amyloid-accumulating mice.

Open-Source Tools for Processing and Analysis of In Vitro Extracellular Neuronal Signals

The recent years have seen unprecedented growth in the manufacturing of neurotechnological tools. The latest technological advancements presented the neuroscientific community with neuronal probes containing thousands of recording sites. These next-generation probes are capable of simultaneously recording neuronal signals from a large number of channels. Numerically, a simple 128-channel neuronal data acquisition system equipped with a 16 bits A/D converter digitizing the acquired analog waveforms at a sampling frequency of 20 kHz will generate approximately 17 GB uncompressed data per hour. Today's biggest challenge is to mine this staggering amount of data and find useful information which can later be used in decoding brain functions, diagnosing diseases, and devising treatments. To this goal, many automated processing and analysis tools have been developed and reported in the literature. A good amount of them are also available as open source for others to adapt them to individual needs. Focusing on extracellularly recorded neuronal signals in vitro, this chapter provides an overview of the popular open-source tools applicable on these signals for spike trains and local field potentials analysis, and spike sorting. Towards the end, several future research directions have also been outlined.

Applications of Deep Learning and Reinforcement Learning to Biological Data

Rapid advances in hardware-based technologies during the past decades have opened up new possibilities for life scientists to gather multimodal data in various application domains, such as omics, bioimaging, medical imaging, and (brain/body)-machine interfaces. These have generated novel opportunities for development of dedicated data-intensive machine learning techniques. In particular, recent research in deep learning (DL), reinforcement learning (RL), and their combination (deep RL) promise to revolutionize the future of artificial intelligence. The growth in computational power accompanied by faster and increased data storage, and declining computing costs have already allowed scientists in various fields to apply these techniques on data sets that were previously intractable owing to their size and complexity. This paper provides a comprehensive survey on the application of DL, RL, and deep RL techniques in mining biological data. In addition, we compare the performances of DL techniques when applied to different data sets across various application domains. Finally, we outline open issues in this challenging research area and discuss future development perspectives.

Sub 100 nW Volatile Nano-Metal-Oxide Memristor as Synaptic-Like Encoder of Neuronal Spikes

Advanced neural interfaces mediate a bioelectronic link between the nervous system and microelectronic devices, bearing great potential as innovative therapy for various diseases. Spikes from a large number of neurons are recorded leading to creation of big data that require online processing under most stringent conditions, such as minimal power dissipation and on-chip space occupancy. Here, we present a new concept where the inherent volatile properties of a nano-scale memristive device are used to detect and compress information on neural spikes as recorded by a multielectrode array. Simultaneously, and similarly to a biological synapse, information on spike amplitude and frequency is transduced in metastable resistive state transitions of the device, which is inherently capable of self-resetting and of continuous encoding of spiking activity. Furthermore, operating the memristor in a very high resistive state range reduces its average in-operando power dissipation to less than 100 nW, demonstrating the potential to build highly scalable, yet energy-efficient on-node processors for advanced neural interfaces.

Intranasal Oxytocin and Vasopressin Modulate Divergent Brainwide Functional Substrates

The neuropeptides oxytocin (OXT) and vasopressin (AVP) have been identified as modulators of emotional social behaviors and associated with neuropsychiatric disorders characterized by social dysfunction. Experimental and therapeutic use of OXT and AVP via the intranasal route is the subject of extensive clinical research. However, the large-scale functional substrates directly engaged by these peptides and their functional dynamics remain elusive. By using cerebral blood volume (CBV) weighted fMRI in the mouse, we show that intranasal administration of OXT rapidly elicits the transient activation of cortical regions and a sustained activation of hippocampal and forebrain areas characterized by high oxytocin receptor density. By contrast, intranasal administration of AVP produced a robust and sustained deactivation in cortico-parietal, thalamic and mesolimbic regions. Importantly, intravenous administration of OXT and AVP did not recapitulate the patterns of modulation produced by intranasal dosing, supporting a central origin of the observed functional changes. In keeping with this notion, hippocampal local field potential recordings revealed multi-band power increases upon intranasal OXT administration. We also show that the selective OXT-derivative TGOT reproduced the pattern of activation elicited by OXT and that the deletion of OXT receptors does not affect AVP-mediated deactivation. Collectively, our data document divergent modulation of brainwide neural systems by intranasal administration of OXT and AVP, an effect that involves key substrates of social and emotional behavior. The observed divergence calls for a deeper investigation of the systems-level mechanisms by which exogenous OXT and AVP modulate brain function and exert their putative therapeutic effects.

Algorithm and software to automatically identify latency and amplitude features of local field potentials recorded in electrophysiological investigation

BACKGROUND

Local field potentials (LFPs) evoked by sensory stimulation are particularly useful in electrophysiological research. For instance, spike timing and current transmembrane current flow estimated from LFPs recorded in the barrel cortex in rats and mice are exploited to investigate how the brain represents sensory stimuli. Recent improvements in microelectrodes technology enable neuroscientists to acquire a great amount of LFPs during the same experimental session, calling for algorithms for their quantitative automatic analysis. Several computer tools were proposed for LFP analysis, but many of them incorporate algorithms that are not open to inspection or modification/personalization. We present a MATLAB software to automatically detect some important LFP features (latency, amplitude, time-derivative value in the inflection-point) for a quantitative analysis. The software features can be customized by the user according to his/her personal research needs. The incorporated algorithm is based on Phillips-Tikhonov regularization to deal with noise amplification due to ill-conditioning. In particular, its accuracy in the estimation of the features of interest is assessed in a Monte Carlo simulation mimicking the acquisition of LFPs in different SNR (signal-to-noise-ratio) conditions. Then, the algorithm is tested by analyzing a real set of 2500 LFPs recorded in rat after whisker stimulation at different depths in the primary somatosensory (S1) cortex, i.e., the region involved in the cortical representation of touch in mammals.

RESULTS

Automatic identification of LFP features by the presented software is easy and fast. As far as accuracy is concerned, error indices from simulated data suggest that the algorithm provides reliable estimates . Indeed, results obtained from LFPs recorded in rat after whisker stimulation are in line with the known sequential activation of the microcircuits of the S1 cortex.

CONCLUSION

A MATLAB software implementing an algorithm to automatically detect the main LFPs features was presented. Simulated and real case studies showed that the employed algorithm is accurate and robust against measurement noise. The available code can be used as it is, but the reported description of the algorithms allows users to easily modify the code to cope with specific requirements.

Early hippocampal hyperexcitability in PS2APP mice: role of mutant PS2 and APP

Alterations of brain network activity are observable in Alzheimer's disease (AD) together with the occurrence of mild cognitive impairment, before overt pathology. However, in humans as well in AD mouse models, identification of early biomarkers of network dysfunction is still at its beginning. We performed in vivo recordings of local field potential activity in the dentate gyrus of PS2APP mice expressing the human amyloid precursor protein (APP) Swedish mutation and the presenilin-2 (PS2) N141I. From a frequency-domain analysis, we uncovered network hyper-synchronicity as early as 3 months, when intracellular accumulation of amyloid beta was also observable. In addition, at 6 months of age, we identified network hyperactivity in the beta/gamma frequency bands, along with increased theta-beta and theta-gamma phase-amplitude cross-frequency coupling, in coincidence with the histopathological traits of the disease. Although hyperactivity and hypersynchronicity were respectively detected in mice expressing the PS2-N141I or the APP Swedish mutant alone, the increase in cross-frequency coupling specifically characterized the 6-month-old PS2APP mice, just before the surge of the cognitive decline.

Real-time encoding and compression of neuronal spikes by metal-oxide memristors

Advanced brain-chip interfaces with numerous recording sites bear great potential for investigation of neuroprosthetic applications. The bottleneck towards achieving an efficient bio-electronic link is the real-time processing of neuronal signals, which imposes excessive requirements on bandwidth, energy and computation capacity. Here we present a unique concept where the intrinsic properties of memristive devices are exploited to compress information on neural spikes in real-time. We demonstrate that the inherent voltage thresholds of metal-oxide memristors can be used for discriminating recorded spiking events from background activity and without resorting to computationally heavy off-line processing. We prove that information on spike amplitude and frequency can be transduced and stored in single devices as non-volatile resistive state transitions. Finally, we show that a memristive device array allows for efficient data compression of signals recorded by a multi-electrode array, demonstrating the technology's potential for building scalable, yet energy-efficient on-node processors for brain-chip interfaces.

The need for intelligent compression of big data, for example in neuroscience, has sparked interest in neuromorphic data processing. Here, Gupta et al. use memristors as event integrators to encode and compress neuronal spiking activity recorded by multi-electrode arrays.

Trends and Challenges in Neuroengineering: Toward "Intelligent" Neuroprostheses through Brain-"Brain Inspired Systems" Communication

Future technologies aiming at restoring and enhancing organs function will intimately rely on near-physiological and energy-efficient communication between living and artificial biomimetic systems. Interfacing brain-inspired devices with the real brain is at the forefront of such emerging field, with the term "neurobiohybrids" indicating all those systems where such interaction is established. We argue that achieving a "high-level" communication and functional synergy between natural and artificial neuronal networks in vivo, will allow the development of a heterogeneous world of neurobiohybrids, which will include "living robots" but will also embrace "intelligent" neuroprostheses for augmentation of brain function. The societal and economical impact of intelligent neuroprostheses is likely to be potentially strong, as they will offer novel therapeutic perspectives for a number of diseases, and going beyond classical pharmaceutical schemes. However, they will unavoidably raise fundamental ethical questions on the intermingling between man and machine and more specifically, on how deeply it should be allowed that brain processing is affected by implanted "intelligent" artificial systems. Following this perspective, we provide the reader with insights on ongoing developments and trends in the field of neurobiohybrids. We address the topic also from a "community building" perspective, showing through a quantitative bibliographic analysis, how scientists working on the engineering of brain-inspired devices and brain-machine interfaces are increasing their interactions. We foresee that such trend preludes to a formidable technological and scientific revolution in brain-machine communication and to the opening of new avenues for restoring or even augmenting brain function for therapeutic purposes.

Anesthesia effect on single local field potentials variability in rat barrel cortex: Preliminary results

The rat barrel cortex is a widely used model of information processing in the somatosensory area, thanks to its precise and easily recognizable organization. However, evoked Local Field Potentials (LFPs) generated in the barrel cortex by repetitive deflections of rat whiskers show large variability in shapes and timings. Moreover, anesthetics can deeply affect the profile of evoked responses. This paper presents preliminary report on the variability and the effect of commonly used anesthetics on these signals. We studied representative signal shape characteristics (e.g., latency and amplitude of events) extracted from evoked responses acquired by means of standard Ag/AgCl electrodes from different cortical layers. As an early result, we found significant difference in the latency of the first principal peak of the responses. Under Tiletamine-Xylazine anesthetic, the responses or events of the evoked LFPs occurred later than the ones recorded while urethane was administered. Furthermore, the distributions of the peak latencies in all cortical layers were narrower in case of Urethane. This behavior should be attributed to the different effects of these two anesthetics on specific synaptic receptors and thus on the processing of neural information and the encoding of sensory input along the cortical pathway.

A tunable local field potentials computer simulator to assess minimal requirements for phase-amplitude cross-frequency-coupling estimation

The quantitative study of cross-frequency coupling (CFC) is a relevant issue in neuroscience. In local field potentials (LFPs), measured either in the cortex or in the hippocampus, how γ-oscillation amplitude is modulated by changes in θ-rhythms-phase is thought to be important in memory formation. Several methods were proposed to quantify CFC, but reported evidence suggests that experimental parameters affect the results. Therefore, a simulation tool to support the determination of minimal requirements for CFC estimation in order to obtain reliable results is particularly useful. An approach to generate computer-simulated signals having CFC intensity, sweep duration, signal-to-noise ratio (SNR), and multiphasic-coupling tunable by the user has been developed. Its utility has been proved by a study evaluating minimal sweep duration and SNR required for reliable θ-γ CFC estimation from signals simulating LFP measured in the mouse hippocampus. A MATLAB® software was made available to facilitate methodology reproducibility. The analysis of the synthetic LFPs created by the simulator shows how the minimal sweep duration for achieving accurate θ-γ CFC estimates increases as SNR decreases and the number of CFC levels to discriminate increases. In particular, a sufficient reliability in discriminating five different predetermined CFC levels is reached with 35-s sweep with SNR = 20, while SNR = 5 requires at least 140-s sweep.

Processing and Analysis of Multichannel Extracellular Neuronal Signals: State-of-the-Art and Challenges

In recent years multichannel neuronal signal acquisition systems have allowed scientists to focus on research questions which were otherwise impossible. They act as a powerful means to study brain (dys)functions in in-vivo and in in-vitro animal models. Typically, each session of electrophysiological experiments with multichannel data acquisition systems generate large amount of raw data. For example, a 128 channel signal acquisition system with 16 bits A/D conversion and 20 kHz sampling rate will generate approximately 17 GB data per hour (uncompressed). This poses an important and challenging problem of inferring conclusions from the large amounts of acquired data. Thus, automated signal processing and analysis tools are becoming a key component in neuroscience research, facilitating extraction of relevant information from neuronal recordings in a reasonable time. The purpose of this review is to introduce the reader to the current state-of-the-art of open-source packages for (semi)automated processing and analysis of multichannel extracellular neuronal signals (i.e., neuronal spikes, local field potentials, electroencephalogram, etc.), and the existing Neuroinformatics infrastructure for tool and data sharing. The review is concluded by pinpointing some major challenges that are being faced, which include the development of novel benchmarking techniques, cloud-based distributed processing and analysis tools, as well as defining novel means to share and standardize data.

Differential Modulation of Excitatory and Inhibitory Neurons during Periodic Stimulation

Non-invasive transcranial neuronal stimulation, in addition to deep brain stimulation, is seen as a promising therapeutic and diagnostic approach for an increasing number of neurological diseases such as epilepsy, cluster headaches, depression, specific type of blindness, and other central nervous system disfunctions. Improving its effectiveness and widening its range of use may strongly rely on development of proper stimulation protocols that are tailored to specific brain circuits and that are based on a deep knowledge of different neuron types response to stimulation. To this aim, we have performed a simulation study on the behavior of excitatory and inhibitory neurons subject to sinusoidal stimulation. Due to the intrinsic difference in membrane conductance properties of excitatory and inhibitory neurons, we show that their firing is differentially modulated by the wave parameters. We analyzed the behavior of the two neuronal types for a broad range of stimulus frequency and amplitude and demonstrated that, within a small-world network prototype, parameters tuning allow for a selective enhancement or suppression of the excitation/inhibition ratio.

Automated analysis of local field potentials evoked by mechanical whisker stimulation in rat barrel cortex

Local field potentials (LFPs) recorded in the barrel cortex in rats and mice are important to investigate somatosensory systems, the final aim being to start to understand mechanisms of brain representation of sensory stimuli in humans. Parameters extracted from LFP of particular interest include spike timing and transmembrane current flow. Recent improvements in microelectrodes technology have enabled neuroscientists to acquire a great amount of LFP signals during the same experimental session, calling for the development of algorithms for their quantitative automatic analysis. In the present work, an algorithm based on Phillips-Tikhonov regularization is presented to automatically detect the main features (in terms of amplitude and latency) of LFP waveforms recorded after whisker stimulation in rat. The accuracy of the algorithm is first assessed in a Monte Carlo simulation mimicking the acquisition of LFP in three different conditions of SNR. Then, the algorithm is tested by analyzing a set of 100 LFP recorded in the primary somatosensory (S1) cortex, i.e., the region involved in the cortical representation of touch in mammals.

The role of miR-29 family members in malignant hematopoiesis

AIMS

MicroRNAs of the miR-29 family members were one of the first microRNAs identified as possible therapeutic agents in malignant hematopoiesis. The aim of our review is to summarize the current state of knowledge on miR-29 family members.

METHODS

We performed literature searches involving miR-29 family members and their relationship to individual hematological malignancies, namely acute myeloid leukemia (AML), chronic lymphoblastic leukemia (CLL) and chronic myeloid leukemia (CML). We also searched for subgroups of hematological malignancies, e.g. multiple myeloma, that are regarded as members of the acute or chronic types of leukemias.

RESULTS

A number of genes appear to be regulated by miR-29 family members in various physiological and pathological situations. In our view regulation of Tcl-1, Mcl-1 and DNA methyltransferases is relevant in case of hematological malignancies, hence these are the focus of this review. miR-29 family members also function during normal T-cell and B-cell development.

CONCLUSION

MiR-29 family members appear to govern some general features in commonly heterogenous hematological malignancies and therefore form a potential target for treatment.

Mechanisms underlying the attachment and spreading of human osteoblasts: from transient interactions to focal adhesions on vitronectin-grafted bioactive surfaces

The features of implant devices and the reactions of bone-derived cells to foreign surfaces determine implant success during osseointegration. In an attempt to better understand the mechanisms underlying osteoblasts attachment and spreading, in this study adhesive peptides containing the fibronectin sequence motif for integrin binding (Arg-Gly-Asp, RGD) or mapping the human vitronectin protein (HVP) were grafted on glass and titanium surfaces with or without chemically induced controlled immobilization. As shown by total internal reflection fluorescence microscopy, human osteoblasts develop adhesion patches only on specifically immobilized peptides. Indeed, cells quickly develop focal adhesions on RGD-grafted surfaces, while HVP peptide promotes filopodia, structures involved in cellular spreading. As indicated by immunocytochemistry and quantitative polymerase chain reaction, focal adhesions kinase activation is delayed on HVP peptides with respect to RGD while an osteogenic phenotypic response appears within 24h on osteoblasts cultured on both peptides. Cellular pathways underlying osteoblasts attachment are, however, different. As demonstrated by adhesion blocking assays, integrins are mainly involved in osteoblast adhesion to RGD peptide, while HVP selects osteoblasts for attachment through proteoglycan-mediated interactions. Thus an interfacial layer of an endosseous device grafted with specifically immobilized HVP peptide not only selects the attachment and supports differentiation of osteoblasts but also promotes cellular migration.

Na+ channels at postsynaptic muscle membrane affects synaptic transmission at neuromuscular junction: a simulation study

Motor movement is controlled by the brain through transmitting electrochemical signals to the muscle fibers that cause the contraction of the muscles. A motoneuron carrying the impulse creates a synapse with the muscle fiber which is known as Neuromuscular Junction (NMJ). The muscle infolds taking part in the synapse contains large amount of sodium channels. The current that passes through the narrow synaptic cleft affects the adjacent membranes electrical properties in turn modifies the synaptic transmission process. Considering this phenomenon we have studied the effect of sodium channels at the NMJ to find out its effect in the generation of extracellular potentials at the synaptic cleft of the junction. Through simulation results we confirm that the conductivity of the sodium channels present at the postsynaptic muscle membrane and the junction height affect the generation of the extracellular potentials at the junction which modifies the synaptic properties of the NMJ.

Sodium channel β2 subunit promotes filopodia-like processes and expansion of the dendritic tree in developing rat hippocampal neurons

The β2 auxiliary subunit of voltage-gated sodium channels (VGSCs) appears at early stages of brain development. It is abundantly expressed in the mammalian central nervous system where it forms complexes with different channel isoforms, including Na_v1.2. From the structural point of view, β2 is a transmembrane protein: at its extracellular N-terminus an Ig-like type C2 domain mediates the binding to the pore-forming alpha subunit with disulfide bonds and the interactions with the extracellular matrix. Given this structural versatility, β2 has been suggested to play multiple functions ranging from channel targeting to the plasma membrane and gating modulation to control of cell adhesion. We report that, when expressed in Chinese Hamster Ovary cells CHO-K1, the subunit accumulates at the perimetral region of adhesion and particularly in large lamellipodia-like membrane processes where it induces formation of filopodia-like structures. When overexpressed in developing embryonic rat hippocampal neurons in vitro, β2 specifically promotes formation of filopodia-like processes in dendrites leading to expansion of the arborization tree, while axonal branching remains unaltered. In contrast to this striking and highly specific effect on dendritic morphology, the targeting of functional sodium channels to the plasma membrane, including the preferential localization of Na_v1.2 at the axon, and their gating properties are only minimally affected. From these and previously reported observations it is suggested that β2, among its multiple functions, may contribute to promote dendritic outgrowth and to regulate neuronal wiring at specific stages of neuronal development.

An automated method to determine angular preferentiality using LFPs recorded from rat barrel cortex by brain-chip interface under mechanical whisker stimulation

The sensory information processing in the rodents is mainly done by whisking, through which they explore the environment, perform object localization, texture and shape discrimination very precisely. During whisking, microcircuits in the corresponding barrel columns get activated to segregate and integrate the tactile information through the information processing pathway. To primarily understand the whisking mechanism angular preferentiality determination is very important. In this work we propose an automated method to determine different events present in the local field potentials (LFPs), calculate latencies and amplitudes related to those events and use them along with the stimulation angle information to determine the angular preferentiality. The method is extensively tested on LFPs recorded from S1 barrel cortex of anesthetized rats using EOSFET (Electrolyte-Oxide-Semiconductor Field Effect Transistor) based neuronal probes.

SigMate: a Matlab-based automated tool for extracellular neuronal signal processing and analysis

Rapid advances in neuronal probe technology for multisite recording of brain activity have posed a significant challenge to neuroscientists for processing and analyzing the recorded signals. To be able to infer meaningful conclusions quickly and accurately from large datasets, automated and sophisticated signal processing and analysis tools are required. This paper presents a Matlab-based novel tool, "SigMate", incorporating standard methods to analyze spikes and EEG signals, and in-house solutions for local field potentials (LFPs) analysis. Available modules at present are - 1. In-house developed algorithms for: data display (2D and 3D), file operations (file splitting, file concatenation, and file column rearranging), baseline correction, slow stimulus artifact removal, noise characterization and signal quality assessment, current source density (CSD) analysis, latency estimation from LFPs and CSDs, determination of cortical layer activation order using LFPs and CSDs, and single LFP clustering; 2. Existing modules: spike detection, sorting and spike train analysis, and EEG signal analysis. SigMate has the flexibility of analyzing multichannel signals as well as signals from multiple recording sources. The in-house developed tools for LFP analysis have been extensively tested with signals recorded using standard extracellular recording electrode, and planar and implantable multi transistor array (MTA) based neural probes. SigMate will be disseminated shortly to the neuroscience community under the open-source GNU-General Public License.

Effect of self-gating on action potential firing at neuromuscular junction

The neuromuscular junction (NMJ) is the place where the axon terminal of motoneuron connects the 'endplate' of a muscle fiber. During this transduction a large depolarization (endplate potential) caused by the nerve impulse opens a large number of voltage-sensitive sodium channels at the post-junctional terminal. As a result, action potentials are generated and propagated along the muscle fiber causing contraction. This work shows simulated results of the voltage-dependent sodium channels' firing behavior at the NMJ using a mathematical model. It is found that the firing behavior of the sodium channels change basing on their activation and inactivation kinetics which are highly influenced by the self-gating behavior of the sodium conductances. The simulation results showed that self-gating of sodium channels increase conduction efficiency at the NMJ and decrease threshold for firing.

Automatic detection of layer activation order in information processing pathways of rat barrel cortex under mechanical whisker stimulation

Whisking is the natural way by which rodents explore the environment. During whisking, microcircuits in the corresponding barrel columns get activated to segregate and integrate the tactile information through the information processing pathway. The local field potentials (LFPs) recorded from the barrel columns provide important information about this pathway. Different layers of the cortex get activated during this information processing, thus having precise information about the order of layer activation is desired. This work proposes an automated, computationally efficient and easy to implement method to determine the cortical layer activation for the signals recorded from barrel cortex of anesthetized rats upon mechanical whisker stimulation.

Stimulation of Ca²+ signals in neurons by electrically coupled electrolyte-oxide-semiconductor capacitors

Electrolyte-oxide-semiconductor capacitors (EOSCs) are a class of microtransducers for extracellular electrical stimulation that have been successfully employed to activate voltage-dependent sodium channels at the neuronal soma to generate action potentials in vitro. In the present work, we report on their use to control Ca²+ signalling in cultured mammalian cells, including neurons. Evidence is provided that EOSC stimulation with voltage waveforms in the microsecond or nanosecond range activates two distinct Ca²+ pathways, either by triggering Ca²+ entry through the plasma membrane or its release from intracellular stores. Ca²+ signals were activated in non-neuronal and neuronal cell lines, CHO-K1 and SH-SY5Y. On this basis, stimulation was tailored to rat and bovine neurons to mimic physiological somatic Ca²+ transients evoked by glutamate. Being minimally invasive and easy to use, the new method represents a versatile complement to standard electrophysiology and imaging techniques for the investigation of Ca²+ signalling in dissociated primary neurons and cell lines.

SigMate: A MATLAB-based neuronal signal processing tool

Advances in neuronal probe technology to record brain activity have posed a significant challenge in performing necessary processing and analysis of the recorded data. To be able to infer meaningful conclusions from the recorded signals through these probes, sophisticated signal processing and analysis tools are required. This paper presents a MATLAB-based novel tool, 'SigMate', capable of performing various processing and analysis incorporating the available standard tools and our in-house custom tools. The present features include, data display (2D and 3D), baseline correction, stimulus artifact removal, noise characterization, file operations (file splitter, file concatenator, and file column rearranger), latency estimation, determination of cortical layer activation order, spike detection, spike sorting, and are gradually growing. This tool has been tested extensively for the recordings using the standard micropipettes as well as implantable neural probes based on EOSFETs (Electrolyte-Oxide-Semiconductor Field Effect Transistors) and will be made available to the community shortly.

Space and time-resolved gene expression experiments on cultured mammalian cells by a single-cell electroporation microarray

Single-cell experiments represent the next frontier for biochemical and gene expression research. Although bulk-scale methods averaging populations of cells have been traditionally used to investigate cellular behavior, they mask individual cell features and can lead to misleading or insufficient biological results. We report on a single-cell electroporation microarray enabling the transfection of pre-selected individual cells at different sites within the same culture (space-resolved), at arbitrarily chosen time points and even sequentially to the same cells (time-resolved). Delivery of impermeant molecules by single-cell electroporation was first proven to be finely tunable by acting on the electroporation protocol and then optimized for transfection of nucleic acids into Chinese Hamster Ovary (CHO-K1) cells. We focused on DNA oligonucleotides (ODNs), short interfering RNAs (siRNAs), and DNA plasmid vectors, thus providing a versatile and easy-to-use platform for time-resolved gene expression experiments in single mammalian cells.

A potential role for the vanilloid receptor TRPV1 in the therapeutic effect of curcumin in dinitrobenzene sulphonic acid-induced colitis in mice

A protective role of the transient potential vanilloid receptor 1 (TRPV1) in intestinal inflammation induced by dinitrobenzene sulphonic acid (DNBS) has been recently demonstrated. Curcumin, the major active component of turmeric, is also able to prevent and ameliorate the severity of the damage in DNBS-induced colitis. We evaluated the possibility that curcumin (45 mg kg(-1) day p.o. for 2 days before and 5 days after the induction of colitis) was able to reduce DNBS-induced colitis in mice, by acting as a TRPV1 agonist. Macroscopic damage score, histological damage score and colonic myeloperoxidase (MPO) activity were significantly lower (by 71%, 65% and 73%, respectively; P < 0.01), in animals treated with curcumin compared with untreated animals. Capsazepine (30 mg kg(-1), i.p.), a TRPV1 receptor antagonist, completely abolished the protective effects of curcumin. To extend these data in vitro, Xenopus oocytes expressing rat TRPV1 were examined. Capsaicin-evoked currents (3.3 micromol L(-1)) disappeared subsequent either to removal of the agonist or subsequent to the addition of capsazepine. However, curcumin (30 micromol L(-1)) was ineffective both as regard direct modification of cell membrane currents and as regard interference with capsaicin-mediated effects. As sensitization of the TRPV1 receptor by mediators of inflammation in damaged tissues has been shown previously, our results suggest that in inflamed, but not in normal tissue, curcumin can interact with the TRPV1 receptor to mediate its protective action in DNBS-induced colitis.

A potential role for the vanilloid receptor TRPV1 in the therapeutic effect of curcumin in dinitrobenzene sulphonic acid-induced colitis in mice

A protective role of the transient potential vanilloid receptor 1 (TRPV1) in intestinal inflammation induced by dinitrobenzene sulphonic acid (DNBS) has been recently demonstrated. Curcumin, the major active component of turmeric, is also able to prevent and ameliorate the severity of the damage in DNBS-induced colitis. We evaluated the possibility that curcumin (45 mg kg(-1) day p.o. for 2 days before and 5 days after the induction of colitis) was able to reduce DNBS-induced colitis in mice, by acting as a TRPV1 agonist. Macroscopic damage score, histological damage score and colonic myeloperoxidase (MPO) activity were significantly lower (by 71%, 65% and 73%, respectively; P < 0.01), in animals treated with curcumin compared with untreated animals. Capsazepine (30 mg kg(-1), i.p.), a TRPV1 receptor antagonist, completely abolished the protective effects of curcumin. To extend these data in vitro, Xenopus oocytes expressing rat TRPV1 were examined. Capsaicin-evoked currents (3.3 micromol L(-1)) disappeared subsequent either to removal of the agonist or subsequent to the addition of capsazepine. However, curcumin (30 micromol L(-1)) was ineffective both as regard direct modification of cell membrane currents and as regard interference with capsaicin-mediated effects. As sensitization of the TRPV1 receptor by mediators of inflammation in damaged tissues has been shown previously, our results suggest that in inflamed, but not in normal tissue, curcumin can interact with the TRPV1 receptor to mediate its protective action in DNBS-induced colitis.

Peculiar labeling of cultured hippocampal neurons by different sera harboring anti-glutamic acid decarboxylase autoantibodies (GAD-Ab)

Immunological derangement is assumed to be present in a subgroup of patients affected by drug-resistant epilepsy with serum harboring anti-glutamic acid decarboxylase autoantibodies (GAD-Ab). To further investigate the specific reactivity of GAD-Ab with target cells, we tested sera from drug-resistant epileptics harboring GAD-Ab on cultured fetal rat hippocampal neurons. As a control, we tested sera from GAD-Ab-negative epileptics and GAD-Ab-positive patients affected by Stiff Person Syndrome (SPS), ataxia or diabetes. A specific pattern of reactivity, varying according to disease, was detected on application of sera from GAD-Ab-positive patients with epilepsy, SPS and ataxia, but no specific labeling was found on application of sera from patients with GAD-Ab-negative epilepsy or from GAD-Ab-positive diabetic controls.

Potassium channel gating in adhesion: from an oocyte-silicon to a neuron-astrocyte adhesion contact

In a neuron-astrocyte adhesion contact the ionic current due to the opening of voltage-dependent potassium channels has to flow along a narrow intercellular cleft, generating there an extracellular voltage. This voltage might be large enough to affect significantly the dependence of channel gating from the intracellular voltage. In order to test this hypothesis, we considered a Xenopus oocyte expressing voltage-dependent potassium channels adhering to a layer of silicon oxide as a simplified model of cell-cell adhesion; here the cell membrane and silicon oxide are separated by a narrow cleft and form a junction of circular shape. We measured directly the extracellular voltage along the diameter of the cleft and investigated its effect on channel gating using a linear array of field effect transistors integrated in the silicon substrate. On this experimental basis we demonstrated that the voltage dependence of potassium channels is strongly affected by adhesion, as can be predicted using a model of a two-dimensional cable and electrodiffusion theory. Computations based on the model showed that along a neuron-astrocyte adhesion contact the opening of voltage-dependent Kv2.1 potassium channels is significantly reduced with respect to identical channels facing an open extracellular space.

Increase in cytosolic Ca2+ induced by elevation of extracellular Ca2+ in skeletal myogenic cells

Cytoplasmic Ca(2+) concentration ([Ca(2+)](i)) variation is a key event in myoblast differentiation, but the mechanism by which it occurs is still debated. Here we show that increases of extracellular Ca(2+) concentration ([Ca(2+)](o)) produced membrane hyperpolarization and a concentration-dependent increase of [Ca(2+)](i) due to Ca(2+) influx across the plasma membrane. Responses were not related to inositol phosphate turnover and Ca(2+)-sensing receptor. [Ca(2+)](o)-induced [Ca(2+)](i) increase was inhibited by Ca(2+) channel inhibitors and appeared to be modulated by several kinase activities. [Ca(2+)](i) increase was potentiated by depletion of intracellular Ca(2+) stores and depressed by inactivation of the Na(+)/Ca(2+) exchanger. The response to arginine vasopressin (AVP), which induces inositol 1,4,5-trisphosphate-dependent [Ca(2+)](i) increase in L6-C5 cells, was not modified by high [Ca(2+)](o). On the contrary, AVP potentiated the [Ca(2+)](i) increase in the presence of elevated [Ca(2+)](o). Other clones of the L6 line as well as the rhabdomyosarcoma RD cell line and the satellite cell-derived C2-C12 line expressed similar responses to high [Ca(2+)](o), and the amplitude of the responses was correlated with the myogenic potential of the cells.

Dynamic localization and clustering of dendritic Kv2.1 voltage-dependent potassium channels in developing hippocampal neurons

Dendritic excitability is modulated by the highly variable spatial and temporal expression pattern of voltage-dependent potassium channels. Somatodendritic Kv2.1 channels contribute a major component of delayed rectifier potassium current in cultured hippocampal neurons, where Kv2.1 is localized to large clusters on the soma and proximal dendrites. Here we found that dramatic differences exist in the clustering of endogenous Kv2.1 in cultured rat hippocampal GABAergic interneurons and glutamatergic pyramidal neurons. Studies on neurons developing in culture revealed that while a similar sequence of Kv2.1 localization and clustering occurred in both cell types, the process was temporally delayed in pyramidal cells. Localization and clustering of recombinant green fluorescent protein-tagged Kv2.1 occurred by the same sequence of events, and imaging of GFP-Kv2.1 clustering in living neurons revealed dynamic fusion events that underlie cluster formation. Overexpression of GFP-Kv2.1 accelerated the clustering program in pyramidal neurons such that the observed differences in Kv2.1 clustering in pyramidal neurons and interneurons were eliminated. Confocal imaging showed a preferential association of Kv2.1 with the basal membrane in cultured neurons, and electrophysiological recordings from neurons cultured on transistors revealed that Kv2.1 contributed the bulk of a previously described adherens junction delayed rectifier potassium conductance. Finally, Kv2.1 clusters were found spatially associated with ryanodine receptor intracellular Ca(2+) ([Ca(2+)](i)) release channels. These findings reveal a stepwise assembly of Kv2.1 potassium channels into membrane clusters during development, and an association of these clusters with Ca(2+) signaling apparatus. Together these data suggest that the restricted localization of Kv2.1 may play an important role in the previously observed contribution of this potassium channel in regulating dendritic [Ca(2+)](i) transients.

Transistor probes local potassium conductances in the adhesion region of cultured rat hippocampal neurons

Adhesion interactions of neurons in a tissue may affect the ion conductance of the plasma membrane, inducing selective localization and modulation of channels. We studied the adhesion region of cultured neurons from rat hippocampus as a defined model where such effects could be observed electrophysiologically, taking advantage of extracellular recording by a transistor integrated in the substrate. We observed the K(+) current through the region of soma adhesion under voltage-clamp and compared it with the current through the whole cell. We found that the specific A-type conductance was depleted, even completely, in the region of adhesion, whereas the specific K-type conductance was enhanced up to a factor of 12. The electrophysiological approach opens a new way to investigate targeting of ion channels in the cell membrane as a function of adhesion processes.