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000278929 1001_ $$0P:(DE-2719)9001361$$aEmery, Brett Addison$$b0$$eFirst author$$udzne
000278929 245__ $$aMEA-seqX: High-Resolution Profiling of Large-Scale Electrophysiological and Transcriptional Network Dynamics.
000278929 260__ $$aWeinheim$$bWiley-VCH$$c2025
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000278929 520__ $$aConcepts of brain function imply congruence and mutual causal influence between molecular events and neuronal activity. Decoding entangled information from concurrent molecular and electrophysiological network events demands innovative methodology bridging scales and modalities. The MEA-seqX platform, integrating high-density microelectrode arrays, spatial transcriptomics, optical imaging, and advanced computational strategies, enables the simultaneous recording and analysis of molecular and electrical network activities at mesoscale spatial resolution. Applied to a mouse hippocampal model of experience-dependent plasticity, MEA-seqX unveils massively enhanced nested dynamics between transcription and function. Graph-theoretic analysis reveals an increase in densely connected bimodal hubs, marking the first observation of coordinated hippocampal circuitry dynamics at molecular and functional levels. This platform also identifies different cell types based on their distinct bimodal profiles. Machine-learning algorithms accurately predict network-wide electrophysiological activity features from spatial gene expression, demonstrating a previously inaccessible convergence across modalities, time, and scales.
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000278929 650_7 $$2Other$$aAI machine‐learning
000278929 650_7 $$2Other$$aconnectome
000278929 650_7 $$2Other$$aexperience‐dependent plasticity
000278929 650_7 $$2Other$$alarge‐scale neural recordings
000278929 650_7 $$2Other$$apredictive modeling
000278929 650_7 $$2Other$$aspatial transcriptomics
000278929 650_7 $$2Other$$aspatiotemporal dynamics
000278929 650_2 $$2MeSH$$aAnimals
000278929 650_2 $$2MeSH$$aMice
000278929 650_2 $$2MeSH$$aHippocampus: physiology
000278929 650_2 $$2MeSH$$aHippocampus: metabolism
000278929 650_2 $$2MeSH$$aGene Regulatory Networks: genetics
000278929 650_2 $$2MeSH$$aElectrophysiological Phenomena: physiology
000278929 650_2 $$2MeSH$$aGene Expression Profiling: methods
000278929 650_2 $$2MeSH$$aNeuronal Plasticity: physiology
000278929 650_2 $$2MeSH$$aNeurons: physiology
000278929 650_2 $$2MeSH$$aMachine Learning
000278929 7001_ $$0P:(DE-2719)2814182$$aHu, Xin$$b1$$udzne
000278929 7001_ $$0P:(DE-2719)2814292$$aKlütsch, Diana$$b2$$udzne
000278929 7001_ $$0P:(DE-2719)9001867$$aKhanzada, Shahrukh$$b3$$udzne
000278929 7001_ $$aLarsson, Ludvig$$b4
000278929 7001_ $$aDumitru, Ionut$$b5
000278929 7001_ $$aFrisén, Jonas$$b6
000278929 7001_ $$aLundeberg, Joakim$$b7
000278929 7001_ $$0P:(DE-2719)2000011$$aKempermann, Gerd$$b8$$udzne
000278929 7001_ $$0P:(DE-2719)2812628$$aAmin, Hayder$$b9$$eLast author
000278929 773__ $$0PERI:(DE-600)2808093-2$$a10.1002/advs.202412373$$gVol. 12, no. 20, p. 2412373$$n20$$p2412373$$tAdvanced science$$v12$$x2198-3844$$y2025
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