Electrical and chemical modulation of homogeneous and heterogeneous human-iPSCs-derived neuronal networks on high density arrays.

The delicate "Excitatory/Inhibitory balance" between neurons holds significance in neurodegenerative and neurodevelopmental diseases. With the ultimate goal of creating a faithful in vitro model of the human brain, in this study, we investigated the critical factor of heterogeneity, focusing on the interplay between excitatory glutamatergic (E) and inhibitory GABAergic (I) neurons in neural networks. We used high-density Micro-Electrode Arrays (MEA) with 2304 recording electrodes to investigate two neuronal culture configurations: 100% glutamatergic (100E) and 75% glutamatergic / 25% GABAergic (75E25I) neurons. This allowed us to comprehensively characterize the spontaneous electrophysiological activity exhibited by mature cultures at 56 Days in vitro, a time point in which the GABA shift has already occurred. We explored the impact of heterogeneity also through electrical stimulation, revealing that the 100E configuration responded reliably, while the 75E25I required more parameter tuning for improved responses. Chemical stimulation with BIC showed an increase in terms of firing and bursting activity only in the 75E25I condition, while APV and CNQX induced significant alterations on both dynamics and functional connectivity. Our findings advance understanding of diverse neuron interactions and their role in network activity, offering insights for potential therapeutic interventions in neurological conditions. Overall, this work contributes to the development of a valuable human-based in vitro system for studying physiological and pathological conditions, emphasizing the pivotal role of neuron diversity in neural network dynamics.

An efficient deep learning approach to identify dynamics in in vitro neural networks.

Understanding and discriminating the spatiotemporal patterns of activity generated by in vitro and in vivo neuronal networks is a fundamental task in neuroscience and neuroengineering. The state-of-the-art algorithms to describe the neuronal activity mostly rely on global and local well-established spike and burst-related parameters. However, they are not able to capture slight differences in the activity patterns. In this work, we introduce a deep-learning-based algorithm to automatically infer the dynamics exhibited by different neuronal populations. Specifically, we demonstrate that our algorithm is able to discriminate with high accuracy the dynamics of five different populations of in vitro human-derived neural networks with an increasing inhibitory to excitatory neurons ratio.

Investigating the reliability of the evoked response in human iPSCs-derived neuronal networks coupled to micro-electrode arrays.

In vitro models of neuronal networks have emerged as a potent instrument for gaining deeper insights into the intricate mechanisms governing the human brain. Notably, the integration of human-induced pluripotent stem cells (hiPSCs) with micro-electrode arrays offers a means to replicate and dissect both the structural and functional elements of the human brain within a controlled in vitro environment. Given that neuronal communication relies on the emission of electrical (and chemical) stimuli, the employment of electrical stimulation stands as a mean to comprehensively interrogate neuronal assemblies, to better understand their inherent electrophysiological dynamics. However, the establishment of standardized stimulation protocols for cultures derived from hiPSCs is still lacking, thereby hindering the precise delineation of efficacious parameters to elicit responses. To fill this gap, the primary objective of this study resides in delineating effective parameters for the electrical stimulation of hiPSCs-derived neuronal networks, encompassing the determination of voltage amplitude and stimulation frequency able to evoke reliable and stable responses. This study represents a stepping-stone in the exploration of efficacious stimulation parameters, thus broadening the electrophysiological activity profiling of neural networks sourced from human-induced pluripotent stem cells.

Deepening the role of excitation/inhibition balance in human iPSCs-derived neuronal networks coupled to MEAs during long-term development.

Objective.The purpose of this study is to investigate whether and how the balance between excitation and inhibition ('E/I balance') influences the spontaneous development of human-derived neuronal networksin vitro. To achieve that goal, we performed a long-term (98 d) characterization of both homogeneous (only excitatory or inhibitory neurons) and heterogeneous (mixed neuronal types) cultures with controlled E/I ratios (i.e. E:I 0:100, 25:75, 50:50, 75:25, 100:0) by recording their electrophysiological activity using micro-electrode arrays.Approach.Excitatory and inhibitory neurons were derived from human induced pluripotent stem cells (hiPSCs). We realized five different configurations by systematically varying the glutamatergic and GABAergic percentages.Main results.We successfully built both homogeneous and heterogeneous neuronal cultures from hiPSCs finely controlling the E/I ratios; we were able to maintain them for up to 3 months. Homogeneity differentially impacted purely inhibitory (no bursts) and purely excitatory (few bursts) networks, deviating from the typical traits of heterogeneous cultures (burst dominated). Increased inhibition in heterogeneous cultures strongly affected the duration and organization of bursting and network bursting activity. Spike-based functional connectivity and image-based deep learning analysis further confirmed all the above.Significance.Healthy neuronal activity is controlled by a well-defined E/I balance whose alteration could lead to the onset of neurodevelopmental disorders like schizophrenia or epilepsy. Most of the commonly usedin vitromodels are animal-derived or too simplified and thus far from thein vivohuman condition. In this work, by performing a long-term study of hiPSCs-derived neuronal networks obtained from healthy human subjects, we demonstrated the feasibility of a robustin vitromodel which can be further exploited for investigating pathological conditions where the E/I balance is impaired.

SCN1A-deficient excitatory neuronal networks display mutation-specific phenotypes.

Dravet syndrome is a severe epileptic encephalopathy, characterized by (febrile) seizures, behavioural problems and developmental delay. Eighty per cent of patients with Dravet syndrome have a mutation in SCN1A, encoding Nav1.1. Milder clinical phenotypes, such as GEFS+ (generalized epilepsy with febrile seizures plus), can also arise from SCN1A mutations. Predicting the clinical phenotypic outcome based on the type of mutation remains challenging, even when the same mutation is inherited within one family. This clinical and genetic heterogeneity adds to the difficulties of predicting disease progression and tailoring the prescription of anti-seizure medication. Understanding the neuropathology of different SCN1A mutations may help to predict the expected clinical phenotypes and inform the selection of best-fit treatments. Initially, the loss of Na+-current in inhibitory neurons was recognized specifically to result in disinhibition and consequently seizure generation. However, the extent to which excitatory neurons contribute to the pathophysiology is currently debated and might depend on the patient clinical phenotype or the specific SCN1A mutation. To examine the genotype-phenotype correlations of SCN1A mutations in relation to excitatory neurons, we investigated a panel of patient-derived excitatory neuronal networks differentiated on multi-electrode arrays. We included patients with different clinical phenotypes, harbouring various SCN1A mutations, along with a family in which the same mutation led to febrile seizures, GEFS+ or Dravet syndrome. We hitherto describe a previously unidentified functional excitatory neuronal network phenotype in the context of epilepsy, which corresponds to seizurogenic network prediction patterns elicited by proconvulsive compounds. We found that excitatory neuronal networks were affected differently, depending on the type of SCN1A mutation, but did not segregate according to clinical severity. Specifically, loss-of-function mutations could be distinguished from missense mutations, and mutations in the pore domain could be distinguished from mutations in the voltage sensing domain. Furthermore, all patients showed aggravated neuronal network responses at febrile temperatures compared with controls. Finally, retrospective drug screening revealed that anti-seizure medication affected GEFS+ patient- but not Dravet patient-derived neuronal networks in a patient-specific and clinically relevant manner. In conclusion, our results indicate a mutation-specific excitatory neuronal network phenotype, which recapitulates the foremost clinically relevant features, providing future opportunities for precision therapies.

Human neuronal networks on micro-electrode arrays are a highly robust tool to study disease-specific genotype-phenotype correlations in vitro.

Micro-electrode arrays (MEAs) are increasingly used to characterize neuronal network activity of human induced pluripotent stem cell (hiPSC)-derived neurons. Despite their gain in popularity, MEA recordings from hiPSC-derived neuronal networks are not always used to their full potential in respect to experimental design, execution, and data analysis. Therefore, we benchmarked the robustness of MEA-derived neuronal activity patterns from ten healthy individual control lines, and uncover comparable network phenotypes. To achieve standardization, we provide recommendations on experimental design and analysis. With such standardization, MEAs can be used as a reliable platform to distinguish (disease-specific) network phenotypes. In conclusion, we show that MEAs are a powerful and robust tool to uncover functional neuronal network phenotypes from hiPSC-derived neuronal networks, and provide an important resource to advance the hiPSC field toward the use of MEAs for disease phenotyping and drug discovery.

Cold intolerance and neuropathic pain after peripheral nerve injury in upper extremity.

Cold intolerance and pain can be a substantial problem in patients with peripheral nerve injury. We aimed at investigating the relationships among sensory recovery, cold intolerance, and neuropathic pain in patients affected by upper limb peripheral nerve injury (Sunderland type V) treated with microsurgical repair, followed by early sensory re-education. In a cross-sectional clinical study, 100 patients (male/female 81/19; age 40.5 ± 14.8 years and follow-up 17 ± 5 months, mean ± SD), with microsurgical nerve repair and reconstruction in the upper extremity and subsequent early sensory re-education, were evaluated, using Cold Intolerance Symptoms Severity questionnaire-Italian version (CISS-it, cut-off pathology >30/100 points), CISS questionnaire-12 item version (CISS-12, 0-46 points-grouping: healthy that means no cold intolerance [0-14], mild [15-24], moderate [25-34], severe [35-42], very severe [43-46] cold intolerance), probability of neuropathic pain (DouleurNeuropathique-4; [DN4] 4/10), deep and superficial sensibility, tactile threshold (monofilaments), and two-point discrimination (cutoff S2; Medical Research Council scale for sensory function; [MRC-scale]). A high CISS score is associated with possible neuropathic pain (DN4 ≥ 4). Both a low CISS-it score (ie, < 30) and DN4 < 4 is associated with good sensory recovery (MRC ≥ 2). In conclusion patients affected by upper limb peripheral nerve injuries with higher CISS scores more often suffer from cold intolerance and neuropathic pain, and the better their sensory recovery is, the less likely they are to suffer from cold intolerance and neuropathic pain.

Psychometric properties of the Italian version of the Cold Intolerance Symptom Severity questionnaire in upper-extremity nerve repair.

BACKGROUND: In upper-limb peripheral nerve injured patients, cold intolerance is the most bothersome, prolonged and disabling symptom, negatively affecting work and leisure activities. The Cold Intolerance Symptom Severity (CISS) questionnaire is widely used to assess this symptom, though its psychometric properties have not been examined in depth and no validated Italian version exists. AIM: The aim of this study was to examine in depth the psychometric properties of the Italian version of CISS (CISS-It) in a sample of patients with upper-limb peripheral nerve injury. DESIGN: Prospective cross-sectional. SETTING: Outpatient. POPULATION: Seventy-two subjects with upper-limb peripheral nerve injury consecutively admitted for outpatient assessment and rehabilitation. METHODS: We assessed dimensionality, reliability, validity, and responsiveness (minimum detectable change, MDC) of the CISS-It by means of Classical Test Theory methods. RESULTS: Factor analysis confirmed the scale's unidimensionality. Internal consistency (alpha=0.93) and test-retest reliability (ICC2,1 =0.96) were high. The convergent validity of the CISS-It was demonstrated by its correlations with the 4-item Douleur Neuropathique (DN4) Scale (rs=0.73), pain score (rs=0.61), and Medical Research Council (MRC) Scale for sensory function (rs=-0.44). The MDC95 was 11.30 points. CONCLUSIONS: CISS-It is a reliable, valid and easy-to-use questionnaire for measuring cold intolerance in subjects with upper-limb peripheral nerve injury. However, there is room for some refinement of the CISS structure and wording, which we suggest to perform within the framework of modern statistical methods (such as Rasch analysis), in order to optimize content coverage and technical quality of the measurement. CLINICAL REHABILITATION IMPACT: This examination in depth of the psychometric properties of the CISS further increases confidence in the scale's use for clinical assessment and monitoring of abnormal cold sensitivity in the rehabilitation of patients with upper extremity peripheral nerve injury.

Development of a simplified Cold Intolerance Symptom Severity questionnaire in patients with peripheral nerve injury.

The aim of this study was to analyse the Cold Intolerance Symptom Severity (CISS) questionnaire in its Italian validated version, using Rasch analysis, to gain insights for a possible refinement of the questionnaire. The CISS was administered to a convenience sample of 96 consecutively recruited outpatients with upper limb peripheral nerve injury. Data were analysed using Rasch analysis. According to rating scale diagnostics, response options of items 3 and 5 did not comply with the pre-set criteria for an optimal category functioning. After collapsing the malfunctioning categories, all items fitted the measured construct. Principal component analysis of standardized residuals showed local dependence between two items (one of them was considered redundant and deleted); after this deletion, unidimensionality of the 12-item questionnaire (CISS-12) was achieved. The reliability indices of CISS-12 were high (>0.85). Some clearer item wording was introduced in response to comments from an expert panel and patient feedback. Overall, Rasch analysis provided the rationale for improving the measurement qualities of the questionnaire, refining its rating scales, identifying those items most useful for measuring the intended construct and confirming the high reliability of its person-ability and item-difficulty estimates. In conclusion, the new simplified CISS-12 presents robust psychometric properties for measuring cold intolerance in patients with upper limb peripheral nerve injury and represents a solid basis for clinical studies aimed at a precise (interval level) measurement of cold-induced symptoms in these patients.