characterization

The quest for brain identification

In the 17th century, physician Marcello Malpighi observed the existence of distinctive patterns of ridges and sweat glands on fingertips. This was a major breakthrough, and originated a long and continuing quest for ways to uniquely identify individuals based on fingerprints, a technique massively used until today. It is only in the past few years that technologies and methodologies have achieved high-quality measures of an individual’s brain to the extent that personality traits and behavior can be characterized. The concept of “fingerprints of the brain” is very novel and has been boosted thanks to a seminal publication by Finn et al. in 2015. They were among the firsts to show that an individual’s functional brain connectivity profile is both unique and reliable, similarly to a fingerprint, and that it is possible to identify an individual among a large group of subjects solely on the basis of her or his connectivity profile. Yet, the discovery of brain fingerprints opened up a plethora of new questions. In particular, what exactly is the information encoded in brain connectivity patterns that ultimately leads to correctly differentiating someone’s connectome from anybody else’s? In other words, what makes our brains unique? In this talk I am going to partially address these open questions while keeping a personal viewpoint on the subject. I will outline the main findings, discuss potential issues, and propose future directions in the quest for identifiability of human brain networks.

Molecular Characterization of Retinal Cell Types: Insights into Evolutionary Origins and Regional Specializations

Epigenetic rewiring in Schinzel-Giedion syndrome

During life, a variety of specialized cells arise to grant the right and timely corrected functions of tissues and organs. Regulation of chromatin in defining specialized genomic regions (e.g. enhancers) plays a key role in developmental transitions from progenitors into cell lineages. These enhancers, properly topologically positioned in 3D space, ultimately guide the transcriptional programs. It is becoming clear that several pathologies converge in differential enhancer usage with respect to physiological situations. However, why some regulatory regions are physiologically preferred, while some others can emerge in certain conditions, including other fate decisions or diseases, remains obscure. Schinzel-Giedion syndrome (SGS) is a rare disease with symptoms such as severe developmental delay, congenital malformations, progressive brain atrophy, intractable seizures, and infantile death. SGS is caused by mutations in the SETBP1 gene that results in its accumulation further leading to the downstream accumulation of SET. The oncoprotein SET has been found as part of the histone chaperone complex INHAT that blocks the activity of histone acetyltransferases suggesting that SGS may (i) represent a natural model of alternative chromatin regulation and (ii) offer chances to study downstream (mal)adaptive mechanisms. I will present our work on the characterization of SGS in appropriate experimental models including iPSC-derived cultures and mouse.

Convex neural codes in recurrent networks and sensory systems

Neural activity in many sensory systems is organized on low-dimensional manifolds by means of convex receptive fields. Neural codes in these areas are constrained by this organization, as not every neural code is compatible with convex receptive fields. The same codes are also constrained by the structure of the underlying neural network. In my talk I will attempt to provide answers to the following natural questions: (i) How do recurrent circuits generate codes that are compatible with the convexity of receptive fields? (ii) How can we utilize the constraints imposed by the convex receptive field to understand the underlying stimulus space. To answer question (i), we describe the combinatorics of the steady states and fixed points of recurrent networks that satisfy the Dale’s law. It turns out the combinatorics of the fixed points are completely determined by two distinct conditions: (a) the connectivity graph of the network and (b) a spectral condition on the synaptic matrix. We give a characterization of exactly which features of connectivity determine the combinatorics of the fixed points. We also find that a generic recurrent network that satisfies Dale's law outputs convex combinatorial codes. To address question (ii), I will describe methods based on ideas from topology and geometry that take advantage of the convex receptive field properties to infer the dimension of (non-linear) neural representations. I will illustrate the first method by inferring basic features of the neural representations in the mouse olfactory bulb.

A draft connectome for ganglion cell types of the mouse retina

The visual system of the brain is highly parallel in its architecture. This is clearly evident in the outputs of the retina, which arise from neurons called ganglion cells. Work in our lab has shown that mammalian retinas contain more than a dozen distinct types of ganglion cells. Each type appears to filter the retinal image in a unique way and to relay this processed signal to a specific set of targets in the brain. My students and I are working to understand the meaning of this parallel organization through electrophysiological and anatomical studies. We record from light-responsive ganglion cells in vitro using the whole-cell patch method. This allows us to correlate directly the visual response properties, intrinsic electrical behavior, synaptic pharmacology, dendritic morphology and axonal projections of single neurons. Other methods used in the lab include neuroanatomical tracing techniques, single-unit recording and immunohistochemistry. We seek to specify the total number of ganglion cell types, the distinguishing characteristics of each type, and the intraretinal mechanisms (structural, electrical, and synaptic) that shape their stimulus selectivities. Recent work in the lab has identified a bizarre new ganglion cell type that is also a photoreceptor, capable of responding to light even when it is synaptically uncoupled from conventional (rod and cone) photoreceptors. These ganglion cells appear to play a key role in resetting the biological clock. It is just this sort of link, between a specific cell type and a well-defined behavioral or perceptual function, that we seek to establish for the full range of ganglion cell types. My research concerns the structural and functional organization of retinal ganglion cells, the output cells of the retina whose axons make up the optic nerve. Ganglion cells exhibit great diversity both in their morphology and in their responses to light stimuli. On this basis, they are divisible into a large number of types (>15). Each ganglion-cell type appears to send its outputs to a specific set of central visual nuclei. This suggests that ganglion cell heterogeneity has evolved to provide each visual center in the brain with pre-processed representations of the visual scene tailored to its specific functional requirements. Though the outline of this story has been appreciated for some time, it has received little systematic exploration. My laboratory is addressing in parallel three sets of related questions: 1) How many types of ganglion cells are there in a typical mammalian retina and what are their structural and functional characteristics? 2) What combination of synaptic networks and intrinsic membrane properties are responsible for the characteristic light responses of individual types? 3) What do the functional specializations of individual classes contribute to perceptual function or to visually mediated behavior? To pursue these questions, we label retinal ganglion cells by retrograde transport from the brain; analyze in vitro their light responses, intrinsic membrane properties and synaptic pharmacology using the whole-cell patch clamp method; and reveal their morphology with intracellular dyes. Recently, we have discovered a novel ganglion cell in rat retina that is intrinsically photosensitive. These ganglion cells exhibit robust light responses even when all influences from classical photoreceptors (rods and cones) are blocked, either by applying pharmacological agents or by dissociating the ganglion cell from the retina. These photosensitive ganglion cells seem likely to serve as photoreceptors for the photic synchronization of circadian rhythms, the mechanism that allows us to overcome jet lag. They project to the circadian pacemaker of the brain, the suprachiasmatic nucleus of the hypothalamus. Their temporal kinetics, threshold, dynamic range, and spectral tuning all match known properties of the synchronization or "entrainment" mechanism. These photosensitive ganglion cells innervate various other brain targets, such as the midbrain pupillary control center, and apparently contribute to a host of behavioral responses to ambient lighting conditions. These findings help to explain why circadian and pupillary light responses persist in mammals, including humans, with profound disruption of rod and cone function. Ongoing experiments are designed to elucidate the phototransduction mechanism, including the identity of the photopigment and the nature of downstream signaling pathways. In other studies, we seek to provide a more detailed characterization of the photic responsiveness and both morphological and functional evidence concerning possible interactions with conventional rod- and cone-driven retinal circuits. These studies are of potential value in understanding and designing appropriate therapies for jet lag, the negative consequences of shift work, and seasonal affective disorder.

Homeostatic Plasticity in Health and Disease

Dr. Davis will present a summary regarding the identification and characterization of mechanisms of homeostatic plasticity as they relate to the control of synaptic transmission. He will then provide evidence of translation to the mammalian neuromuscular junction and central synapses, and provide tangible links to the etiology of neurological disease.

Towards a More Authentic Vision of the (multi)Coding Potential of RNA

Ten of thousands of open reading frames (ORFs) are hidden within transcripts. They have eluded annotations because they are either small or within unsuspected locations. These are named alternative ORFs (altORFs) or small ORFs and have recently been highlighted by innovative proteogenomic approaches, such as our OpenProt resource, revealing their existence and implications in biological functions. Due to the absence of altORFs from annotations, pathogenic mutations within these are being ignored. I will discuss our latest progress on the re-analysis of large-scale proteomics datasets to improve our knowledge of proteomic diversity, and the functional characterization of a second protein coded by the FUS gene. Finally, I will explain the need to map the coding potential of the transcriptome using artificial intelligence rather than with conventional annotations that do not capture the full translational activity of ribosomes.

NMC4 Short Talk: Maggot brain, mirror image? A statistical analysis of bilateral symmetry in an insect brain connectome

Neuroscientists have many questions about connectomes that revolve around the ability to compare networks. For example, comparing connectomes could help explain how neural wiring is related to individual differences, genetics, disease, development, or learning. One such question is that of bilateral symmetry: are the left and right sides of a connectome the same? Here, we investigate the bilateral symmetry of a recently presented connectome of an insect brain, the Drosophila larva. We approach this question from the perspective of two-sample testing for networks. First, we show how this question of “sameness” can be framed as a variety of different statistical hypotheses, each with different assumptions. Then, we describe test procedures for each of these hypotheses. We show how these different test procedures perform on both the observed connectome as well as a suite of synthetic perturbations to the connectome. We also point out that these tests require careful attention to parameter alignment and differences in network density in order to provide biologically meaningful results. Taken together, these results provide the first statistical characterization of bilateral symmetry for an entire brain at the single-neuron level, while also giving practical recommendations for future comparisons of connectome networks.

NMC4 Short Talk: Synchronization in the Connectome: Metastable oscillatory modes emerge from interactions in the brain spacetime network

The brain exhibits a rich repertoire of oscillatory patterns organized in space, time and frequency. However, despite ever more-detailed characterizations of spectrally-resolved network patterns, the principles governing oscillatory activity at the system-level remain unclear. Here, we propose that the transient emergence of spatially organized brain rhythms are signatures of weakly stable synchronization between subsets of brain areas, naturally occurring at reduced collective frequencies due to the presence of time delays. To test this mechanism, we build a reduced network model representing interactions between local neuronal populations (with damped oscillatory response at 40Hz) coupled in the human neuroanatomical network. Following theoretical predictions, weakly stable cluster synchronization drives a rich repertoire of short-lived (or metastable) oscillatory modes, whose frequency inversely depends on the number of units, the strength of coupling and the propagation times. Despite the significant degree of reduction, we find a range of model parameters where the frequencies of collective oscillations fall in the range of typical brain rhythms, leading to an optimal fit of the power spectra of magnetoencephalographic signals from 89 heathy individuals. These findings provide a mechanistic scenario for the spontaneous emergence of frequency-specific long-range phase-coupling observed in magneto- and electroencephalographic signals as signatures of resonant modes emerging in the space-time structure of the Connectome, reinforcing the importance of incorporating realistic time delays in network models of oscillatory brain activity.

Anatomical and functional characterization of the neuronal circuits underlying ejaculation

During sexual behavior, copulation related sensory information and modulatory signals from the brain must be integrated and converted into the motor and secretory outputs that characterize ejaculation (Lenschow and Lima, Current Opinion in Neurobiology, 2020). Studies in humans and rats suggest the existence of interneurons in the lumbar spinal cord that mediates that step: the spinal ejaculation generator (SEG). My work aimed at gaining mechanistic insights about the neuronal circuits controlling ejaculation thereby applying cutting-edge techniques. More specifically, we mapped anatomically and functionally the spinal circuit for ejaculation starting from the main muscle being involved in sperm expulsion: the bulbospongiosus muscle (BSM). Combining viral tracing strategies with electrophysiology, we specifically show that the BSM motoneurons receive direct synaptic input from a group of interneurons located in between lumbar segment 2 and 3 and expressing the peptide galanin. Electrically and optogenetically activating the galanin positive cells (the SEG) lead to the activation of the motoneurons innervating the BSM and the muscle itself. Finally, inhibition of SEG cells using DREADDs (Designer Receptors Exclusively Activated by Designer Drugs) in sexual behaving animals is currently conducted to reveal whether ejaculation can be prevented.

From genetics to neurobiology through transcriptomic data analysis

Over the past years, genetic studies have uncovered hundreds of genetic variants to be associated with complex brain disorders. While this really represents a big step forward in understanding the genetic etiology of brain disorders, the functional interpretation of these variants remains challenging. We aim to help with the functional characterization of variants through transcriptomic data analysis. For instance, we rely on brain transcriptome atlases, such as Allen Brain Atlases, to infer functional relations between genes. One example of this is the identification of signaling mechanisms of steroid receptors. Further, by integrating brain transcriptome atlases with neuropathology and neuroimaging data, we identify key genes and pathways associated with brain disorders (e.g. Parkinson's disease). With technological advances, we can now profile gene expression in single-cells at large scale. These developments have presented significant computational developments. Our lab focuses on developing scalable methods to identify cells in single-cell data through interactive visualization, scalable clustering, classification, and interpretable trajectory modelling. We also work on methods to integrate single-cell data across studies and technologies.

A geometric framework to predict structure from function in neural networks

The structural connectivity matrix of synaptic weights between neurons is a critical determinant of overall network function. However, quantitative links between neural network structure and function are complex and subtle. For example, many networks can give rise to similar functional responses, and the same network can function differently depending on context. Whether certain patterns of synaptic connectivity are required to generate specific network-level computations is largely unknown. Here we introduce a geometric framework for identifying synaptic connections required by steady-state responses in recurrent networks of rectified-linear neurons. Assuming that the number of specified response patterns does not exceed the number of input synapses, we analytically calculate all feedforward and recurrent connectivity matrices that can generate the specified responses from the network inputs. We then use this analytical characterization to rigorously analyze the solution space geometry and derive certainty conditions guaranteeing a non-zero synapse between neurons.

Deciphering the Dynamics of the Unconscious Brain Under General Anesthesia

General anesthesia is a drug-induced, reversible condition comprised of five behavioral states: unconsciousness, amnesia (loss of memory), antinociception (loss of pain sensation), akinesia (immobility), and hemodynamic stability with control of the stress response. Our work shows that a primary mechanism through which anesthetics create these altered states of arousal is by initiating and maintaining highly structured oscillations. These oscillations impair communication among brain regions. We illustrate this effect by presenting findings from our human studies of general anesthesia using high-density EEG recordings and intracranial recordings. These studies have allowed us to give a detailed characterization of the neurophysiology of loss and recovery of consciousness due to propofol. We show how these dynamics change systematically with different anesthetic classes and with age. As a consequence, we have developed a principled, neuroscience-based paradigm for using the EEG to monitor the brain states of patients receiving general anesthesia. We demonstrate that the state of general anesthesia can be rapidly reversed by activating specific brain circuits. Finally, we demonstrate that the state of general anesthesia can be controlled using closed loop feedback control systems. The success of our research has depended critically on tight coupling of experiments, signal processing research and mathematical modeling.

Defining new multimodal neuroimaging marker for grey matter characterization

The human cortical ribbon varies during the lifespan, from childhood to senescence. To study the effects of genetic and environmental factors on these dynamics, one needs to measure specific phenotypes (cortical volume, surface area, thickness, new neuroimaging phenotypes such as intracortical myelination or multimodal ones based on their combination, or their asymmetries) that characterize the cerebral grey matter accurately

Functional characterization and therapeutic targeting of gene regulatory elements

Residual population dynamics as a window into neural computation

Neural activity in frontal and motor cortices can be considered to be the manifestation of a dynamical system implemented by large neural populations in recurrently connected networks. The computations emerging from such population-level dynamics reflect the interaction between external inputs into a network and its internal, recurrent dynamics. Isolating these two contributions in experimentally recorded neural activity, however, is challenging, limiting the resulting insights into neural computations. I will present an approach to addressing this challenge based on response residuals, i.e. variability in the population trajectory across repetitions of the same task condition. A complete characterization of residual dynamics is well-suited to systematically compare computations across brain areas and tasks, and leads to quantitative predictions about the consequences of small, arbitrary causal perturbations.

Biomedical Image and Genetic Data Analysis with machine learning; applications in neurology and oncology

In this presentation I will show the opportunities and challenges of big data analytics with AI techniques in medical imaging, also in combination with genetic and clinical data. Both conventional machine learning techniques, such as radiomics for tumor characterization, and deep learning techniques for studying brain ageing and prognosis in dementia, will be addressed. Also the concept of deep imaging, a full integration of medical imaging and machine learning, will be discussed. Finally, I will address the challenges of how to successfully integrate these technologies in daily clinical workflow.

K+ Channel Gain of Function in Epilepsy, from Currents to Networks

Recent human gene discovery efforts show that gain-of-function (GOF) variants in the KCNT1gene, which encodes a Na+-activated K+ channel subunit, cause severe epilepsies and other neurodevelopmental disorders. Although the impact of these variants on the biophysical properties of the channels is well characterized, the mechanisms that link channel dysfunction to cellular and network hyperexcitability and human disease are unknown. Furthermore, precision therapies that correct channel biophysics in non-neuronal cells have had limited success in treating human disease, highlighting the need for a deeper understanding of how these variants affect neurons and networks. To address this gap, we developed a new mouse model with a pathogenic human variant knocked into the mouse Kcnt1gene. I will discuss our findings on the in vivo phenotypes of this mouse, focusing on our characterization of epileptiform neural activity using electrophysiology and widefield Ca++imaging. I will also talk about our investigations at the synaptic, cellular, and circuit levels, including the main finding that cortical inhibitory neurons in this model show a reduction in intrinsic excitability and action potential generation. Finally, I will discuss future directions to better understand the mechanisms underlying the cell-type specific effects, as well as the link between the cellular and network level effects of KCNT1 GOF.

Human reconstruction of local image structure from natural scenes

Retinal projections often poorly represent the structure of the physical world: well-defined boundaries within the eye may correspond to irrelevant features of the physical world, while critical features of the physical world may be nearly invisible at the retinal projection. Visual cortex is equipped with specialized mechanisms for sorting these two types of features according to their utility in interpreting the scene, however we know little or nothing about their perceptual computations. I will present novel paradigms for the characterization of these processes in human vision, alongside examples of how the associated empirical results can be combined with targeted models to shape our understanding of the underlying perceptual mechanisms. Although the emerging view is far from complete, it challenges compartmentalized notions of bottom-up/top-down object segmentation, and suggests instead that these two modes are best viewed as an integrated perceptual mechanism.

Functional characterization of human iPSC-derived neurons at single-cell resolution

Recent developments in induced pluripotent stem cell (iPSC) technology have enabled easier access to human cells in vitro. With increasing availability of human iPSC-derived neurons, both healthy and disease cell lines, screening compounds for neurodegenerative diseases on human cells can potentially be performed in the earlier stages of drug discovery. To accelerate the functional characterization of iPSC-derived neurons and the effect of compounds, reproducible and relevant results are necessary. In this webinar, the speakers will: Introduce high-resolution functional imaging of human iPSC-derived neurons Showcase how to extract functional features of hundreds of cells in a cell culture sample label-free Discuss electrophysiological parameters for characterizing the differences among several human neuronal cell lines

MOLECULAR, MORPHOLOGICAL AND ANATOMICAL CHARACTERIZATION OF ENTERIC GLIAL HETEROGENEITY ACROSS THE MOUSE GASTROINTESTINAL TRACT

FENS Forum 2026

CHARACTERIZATION OF A TRANSLATIONAL MOUSE MODEL OF STROKE INTEGRATING ATHEROSCLEROSIS AND CHRONIC NEUROIMMUNE DYSREGULATION

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ADDGENE DATA HUB: A PLATFORM FOR SHARING AAV AND ANTIBODY CHARACTERIZATION DATA

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CHARACTERIZATION OF NEUROPROTECTIVE EFFECTS MEDIATED BY PROLYL ENDOPEPTIDASE-INHIBITORY COMPOUNDS FOR THE TREATMENT OF PARKINSON’S DISEASE

FENS Forum 2026

CHARACTERIZATION OF DOPAMINE RECEPTORS INTERACTIONS ACROSS THE GPCR LANDSCAPE

FENS Forum 2026

BEHAVIORAL CHARACTERIZATION OF AGED <EM>FMR1 </EM>KNOCKOUT MICE. BASAL PHENOTYPING AND SKF81297-MEDIATED DOPAMINERGIC D1/D5 RECEPTOR MODULATION IN AGED FRAGILE X SYNDROME MOUSE MODEL

FENS Forum 2026

BIOPHYSICAL AND HISTOPATHOLOGICAL CHARACTERIZATION OF RFTC LESIONS IN PAG PHANTOMS AND IN VIVO RAT MODELS

FENS Forum 2026

ELECTROPHYSIOLOGICAL CHARACTERIZATION OF NEURONAL ACTIVITY IN CORTICO-LIMBIC CIRCUITS LACKING ASTROCYTIC EXOCYTOSIS

FENS Forum 2026

CHARACTERIZATION OF VOLTAGE-GATED CALCIUM CURRENTS IN ROD BIPOLAR CELLS OF MUTANT CAV1.X MOUSE MODELS

FENS Forum 2026

MULTIMODAL CHARACTERIZATION OF BINGE EATING DISORDER IN WILD-TYPE MICE: BEHAVIORAL, PROTEOMIC AND GUT MICROBIOTA (16S RRNA) ANALYSES

FENS Forum 2026

SURGICAL ACCESS AND ANATOMICAL–HISTOLOGICAL CHARACTERIZATION OF THE PUDENDAL NERVE IN PIGS ​​​​​

FENS Forum 2026

NEUROANATOMICAL CHARACTERIZATION OF NEUROLIGIN-2-MEDIATED GEPHYRIN AGGREGATION

FENS Forum 2026

CHARACTERIZATION OF AXONAL TRANSPORT AND EXTRACELLULAR VESICLE PROFILE IN ALS IPSC-DERIVED MOTOR NEURONS

FENS Forum 2026

<EM>IN VITRO</EM> AND <EM>IN VIVO</EM> CHARACTERIZATION OF HOW<EM> </EM>ALS MUTATION ON THE RNA-BINDING PROTEIN FUS CHANGES GENE EXPRESSION AND SYNAPSES IN HUMAN STEM CELL-DERIVED NEURONS

FENS Forum 2026

EXPANDING THE OPTOGENETIC TOOLBOX: GENOME MINING AND AUTOMATED CHARACTERIZATION BASED DISCOVERY OF RED-SHIFTED CHANNELRHODOPSINS

FENS Forum 2026

BRAIN: 3D CHARACTERIZATION OF BRAIN VASCULATURE AND MICROGLIA TO UNCOVER MORPHO-PHYSIOLOGICAL ALTERATIONS IN ALZHEIMER’S DISEASE MURINE MODELS

FENS Forum 2026

FUNCTIONAL, MORPHOLOGICAL, AND TRANSCRIPTOMIC CHARACTERIZATION OF FUSED PURKINJE CELLS IN THE EAE MOUSE MODEL OF MULTIPLE SCLEROSIS

FENS Forum 2026

CHARACTERIZATION OF PERILESIONAL MOTOR CORTICAL ACTIVITY DURING THE CHRONIC PHASE OF ISCHEMIC STROKE

FENS Forum 2026

HISTOLOGICAL AND ELECTROPHYSIOLOGICAL CHARACTERIZATION OF NEURAL-PRECONDITIONED HUMAN DENTAL PULP STEM CELLS AFTER THEIR GRAFT INTO C57BL/6J HIPPOCAMPUS

FENS Forum 2026

CHARACTERIZATION OF THE ACUTE GENE RESPONSE IN ZEBRAFISH DEMYELINATION MODEL

FENS Forum 2026

STRUCTURAL AND FUNCTIONAL CHARACTERIZATION OF AN OXIME-BASED ACETYLCHOLINESTERASE REACTIVATOR WITH UNEXPECTED GABA AMINOTRANSFERASE INHIBITORY ACTIVITY AT GABAERGIC SYNAPSES

FENS Forum 2026

CYTOSKELETAL DYNAMICS AT SYNAPSES: CHARACTERIZATION OF NOVEL MICROTUBULAR REGULATORS AND THEIR POTENTIAL ROLE IN RESILIENCE TO NEURODEGENERATION

FENS Forum 2026

CHARACTERIZATION OF AXOLOTL SPINAL FLUID DURING HOMEOSTASIS AND LIMB REGENERATION

FENS Forum 2026

FUNCTIONAL CHARACTERIZATION OF A CNS-ENRICHED EXTRANUCLEOLAR SNORNA

FENS Forum 2026

DISTINCT CONNECTIVITY AND FUNCTIONAL CHARACTERIZATION OF HYBRID D1/D2-SPNS IN THE DORSAL STRIATUM

FENS Forum 2026

NEUROANATOMICAL AND FUNCTIONAL CHARACTERIZATION OF THE MOUSE INDUSIUM GRISEUM

FENS Forum 2026

CHARACTERIZATION OF BRAIN INNATE IMMUNE INFILTRATES, ANTIBODY DYNAMICS AND FUNCTIONALITIES IN A MOUSE MODEL OF ANTI-NMDAR ENCEPHALITIS

FENS Forum 2026

ELECTROPHYSIOLOGICAL CHARACTERIZATION OF NA<SUB>V</SUB>1.7 AND K<SUB>V</SUB>7.2/3 CHANNELS IN HUMAN IPSC-DERIVED SENSORY NEURONS

FENS Forum 2026

CHARACTERIZATION OF K<SUB>V</SUB>7.2/7.3, K<SUB>V</SUB>7.2/7.5, AND K<SUB>V</SUB>7.3/7.5 CHANNEL PROPERTIES: ROLES OF SUBUNIT COMPOSITION AND PHARMACOLOGICAL MODULATION

FENS Forum 2026

TIME- AND SEX-DEPENDENT CHARACTERIZATION OF ENGRAM SPINE MORPHOLOGY IN FEAR MEMORY PROCESSING

FENS Forum 2026

PHYSIOLOGICAL CHARACTERIZATION OF VIP NEURONS FROM THE DORSAL RAPHE NUCLEUS AND THEIR ROLE IN NON-MOTOR SYMPTOMS OF PARKINSON’S DISEASE

FENS Forum 2026

SINGLE-CELL CHARACTERIZATION OF NOVEL GABAERGIC SUBTYPES IN THE DEVELOPING BRAINSTEM

FENS Forum 2026

CHARACTERIZATION AND RESCUE OF THE ALTERATIONS IN MOUSE AND HUMAN OLIGODENDROCYTES MODELING THE ACID SPHINGOMYELINASE DEFICIENCY

FENS Forum 2026

FUNCTIONAL CHARACTERIZATION OF SEROTONERGIC TRYPTAMINE ANALOGS VIA CALCIUM IMAGING: POTENCY AND EFFICACY PROFILING AT THE 5-HT₂A RECEPTOR

FENS Forum 2026

<EM>IN VIVO </EM>CHARACTERIZATION OF PV AND SST INTERNEURON DEVELOPMENT IN THE PRIMARY VISUAL CORTEX: IMPLICATIONS FOR NEURODEVELOPMENTAL DISORDERS

FENS Forum 2026

REGION-SPECIFIC ALTERATIONS IN A MALE MOUSE MODEL OF NEUROPATHIC PAIN INDUCED-DEPRESSION: TRANSCRIPTOMIC CHARACTERIZATION

FENS Forum 2026

FUNCTIONAL CHARACTERIZATION OF NEURONAL ACTIVITY AND SYNAPTIC PLASTICITY IN FOREBRAIN AND HIPPOCAMPAL-LIKE ORGANOIDS

FENS Forum 2026

GROUND-TRUTH-BASED CHARACTERIZATION OF ACTIVITY-DEPENDENT SPIKE SHAPE VARIABILITY TO IMPROVE SPIKE SORTING PERFORMANCE

FENS Forum 2026

SPATIAL CHARACTERIZATION OF MYELOID CELL HETEROGENEITY IN ISCHEMIC STROKE UNDER NORMOLIPIDEMIC AND HYPERLIPIDEMIC CONDITIONS

FENS Forum 2026

NEUROCHEMICAL AND PHARMACOKINETIC CHARACTERIZATION OF FENTANYL-INDUCED REWARD MECHANISMS IN MICE

FENS Forum 2026