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Neurons differentiate mechanical stimuli force and rate to elicit unique functional responses, driving the need for further tools to generate various mechanical stimuli. Here, cell-internal nanomagnetic forces (iNMF) are introduced by manipulating internalized magnetic nanoparticles with an external magnetic field across cortical neuron networks in vitro. Under iNMF, cortical neurons exhibit calcium (Ca2+) influx, leading to modulation of activity observed through Ca2+ event rates. Inhibiting particle uptake or altering nanoparticle exposure time reduced the neuronal response to nanomagnetic forces, exposing the requirement of nanoparticle uptake to induce the Ca2+ response. In highly active cortical networks, iNMF robustly modulates synchronous network activity, which is lasting and repeatable. Using pharmacological blockers, it is shown that iNMF activates mechanosensitive ion channels to induce the Ca2+ influx. Then, in contrast to transient mechanically evoked neuronal activity, iNMF activates Ca2+-activated potassium (KCa) channels to stabilize the neuronal membrane potential and induce network activity shifts. The findings reveal the potential of magnetic nanoparticle-mediated mechanical stimulation to modulate neuronal circuit dynamics, providing insights into the biophysics of neuronal computation. 

Archetypal analysis (AA) is a versatile data analysis method to cluster distinct features within a data set. Here, we demonstrate a framework showing the power of AA to spatio-temporally resolve events in calcium imaging, an imaging modality commonly used in neurobiology and neuroscience to capture neuronal communication patterns. After validation of our AA-based approach on synthetic data sets, we were able to characterize neuronal communication patterns in recorded calcium waves. Clinical relevance– Transient calcium events play an essential role in brain cell communication, growth, and network formation, as well as in neurodegeneration. To reliably interpret calcium events from personalized medicine data, where patterns may differ from patient to patient, appropriate image processing and signal analysis methods need to be developed for optimal network characterization. 

Tangential curvatures are a key geometric feature of tissue folds in the human cerebral cortex. In the brain, these smoother and firmer bends are called gyri and sulci and form distinctive curved tissue patterns imposing a mechanical stimulus on neuronal networks. This stimulus is hypothesized to be essential for proper brain cell function but lacks in most standard neuronal cell assays. A variety of soft lithographic micropatterning techniques can be used to integrate round geometries in cell assays. Most microfabricated patterns, however, focus only on a small set of defined curvatures. In contrast, curvatures in the brain span a wide physical range, leaving it unknown which precise role distinct curvatures may play on neuronal cell signaling. Here we report a hydrogel-based multi-curvature design consisting of over twenty bands of distinct parallel curvature ranges to precisely engineer neuronal networks' growth and signaling under patterns of arcs. Monitoring calcium and mitochondrial dynamics in primary rodent neurons grown over two weeks in the multi-curvature patterns, we found that static calcium signaling was locally attenuated under higher curvatures ( k > 0.01 μm −1 ). In contrast, to randomize growth, transient calcium signaling showed higher synchronicity when neurons formed networks in confined multi-curvature patterns. Additionally, we found that mitochondria showed lower motility under high curvatures ( k > 0.01 μm −1 ) than under lower curvatures ( k < 0.01 μm −1 ). Our results demonstrate how sensitive neuronal cell function may be linked and controlled through specific curved geometric features. Furthermore, the hydrogel-based multi-curvature design possesses high compatibility with various surfaces, allowing a flexible integration of geometric features into next-generation neuro devices, cell assays, tissue engineering, and implants.