Experimental models and tools for studying neural–glial instability.
| Model/Tool | What it captures well | Strength for studying epilepsy instability | Main limitation | Best use of this review framework |
|---|---|---|---|---|
| Animal models | In vivo seizure generation, network propagation, cell-type interactions, inflammatory responses, circuit remodeling, longitudinal disease progression | Allows mechanistic study of how neuronal, glial, inflammatory, and circuit-level changes interact over time within intact tissue and behaviorally relevant systems | Species differences, model-specific bias, incomplete representation of human epilepsy heterogeneity, and variable translational fidelity | Best for studying multi-scale disease evolution, epileptogenic transition, and testing causality across cellular and circuit levels [187, 207] |
| In vitro systems | Controlled cellular interactions, defined perturbations, basic excitability changes, glial responses, and mechanistic manipulation under simplified conditions | Useful for isolating specific pathways such as neurotransmitter imbalance, glial regulation, calcium signaling, and inflammatory responses without full in vivo complexity | Limited tissue architecture, reduced long-range network organization, and incomplete representation of chronic disease progression | Best for mechanistic dissection of defined pathways and rapid hypothesis testing under controlled conditions [187, 208] |
| Human tissue studies | Human-relevant cellular pathology, network features in diseased tissue, glial changes, inflammatory signatures, and structural remodeling | Provides direct relevance to human epilepsy biology and can validate whether mechanisms identified in models are present in human epileptic tissue | Limited availability, variable quality, restricted experimental manipulation, and difficulty capturing dynamic progression over time | Best for translational validation of disease-relevant mechanisms and comparison with model-derived findings [102, 209] |
| Organoids/Stem cell-derived systems | Human developmental context, patient-specific biology, cell-type interactions, emerging network behavior, and genetically tractable human-relevant systems | Valuable for linking neural and glial biology to human-specific developmental, genetic, and patient-specific mechanisms of instability | Incomplete maturation, limited vascular/immune complexity, simplified architecture, and uncertain correspondence to full clinical epilepsy states | Best for studying human-relevant mechanisms, genetic vulnerability, and patient-specific neural–glial interactions in controlled systems [198, 210] |
| Calcium imaging | Spatiotemporal activity dynamics, population-level signaling patterns, glial calcium activity, and propagation of network events across cells | Especially useful for visualizing how neuronal and glial activity evolves across space and time during destabilization and seizure-like states | Limited direct measurement of membrane conductance, variable temporal resolution, and interpretive complexity when linking signals to causality | Best for mapping activity coordination, propagation, calcium-linked instability, and neuron–glia signaling dynamics [93, 96] |
| Electrophysiology | Membrane excitability, synaptic transmission, firing properties, inhibitory/excitatory balance, oscillations, and synchronized network discharge | Remains one of the strongest approaches for defining seizure-relevant excitability and testing causal changes in neuronal and circuit behavior | Often narrower in molecular or spatial context unless combined with imaging, molecular profiling, or anatomical analysis | Best for precise measurement of excitability, synaptic function, and network synchronization underlying epileptic instability [187, 208] |
| Omics approaches | Cell-state programs, transcriptional and proteomic heterogeneity, inflammatory signatures, stress pathways, and molecular remodeling across cell types | Powerful for identifying cell-type-specific pathways linking excitability, glial activation, inflammation, and chronic remodeling | Association does not equal causation; findings require physiological and anatomical context for mechanistic interpretation | Best for mapping molecular architecture, heterogeneity, and candidate pathways that can be integrated with functional studies [207, 209] |
| Computational/Network modeling | Multi-scale interaction logic, network dynamics, synchronization rules, parameter testing, and systems-level prediction | Useful for integrating diverse data and exploring how changes in inhibition, glial regulation, calcium signaling, or connectivity may alter network behavior | Dependent on assumptions, model structure, and input quality; may oversimplify biological heterogeneity if not constrained by empirical data | Best for systems-level synthesis, hypothesis generation, and testing how interacting instability mechanisms might produce emergent epileptic behavior [205, 211] |
MMN: Conceptualization, Writing—original draft, Writing—review & editing. The author read and approved the submitted version.
The author declares that there are no conflicts of interest.
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