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@ARTICLE{Emery:278929,
      author       = {Emery, Brett Addison and Hu, Xin and Klütsch, Diana and
                      Khanzada, Shahrukh and Larsson, Ludvig and Dumitru, Ionut
                      and Frisén, Jonas and Lundeberg, Joakim and Kempermann,
                      Gerd and Amin, Hayder},
      title        = {{MEA}-seq{X}: {H}igh-{R}esolution {P}rofiling of
                      {L}arge-{S}cale {E}lectrophysiological and {T}ranscriptional
                      {N}etwork {D}ynamics.},
      journal      = {Advanced science},
      volume       = {12},
      number       = {20},
      issn         = {2198-3844},
      address      = {Weinheim},
      publisher    = {Wiley-VCH},
      reportid     = {DZNE-2025-00655},
      pages        = {2412373},
      year         = {2025},
      abstract     = {Concepts 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.},
      keywords     = {Animals / Mice / Hippocampus: physiology / Hippocampus:
                      metabolism / Gene Regulatory Networks: genetics /
                      Electrophysiological Phenomena: physiology / Gene Expression
                      Profiling: methods / Neuronal Plasticity: physiology /
                      Neurons: physiology / Machine Learning / AI
                      machine‐learning (Other) / connectome (Other) /
                      experience‐dependent plasticity (Other) / large‐scale
                      neural recordings (Other) / predictive modeling (Other) /
                      spatial transcriptomics (Other) / spatiotemporal dynamics
                      (Other)},
      cin          = {AG Amin / AG Kempermann},
      ddc          = {624},
      cid          = {I:(DE-2719)1710010 / I:(DE-2719)1710001},
      pnm          = {351 - Brain Function (POF4-351) / 352 - Disease Mechanisms
                      (POF4-352)},
      pid          = {G:(DE-HGF)POF4-351 / G:(DE-HGF)POF4-352},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {pmid:40304297},
      pmc          = {pmc:PMC12120740},
      doi          = {10.1002/advs.202412373},
      url          = {https://pub.dzne.de/record/278929},
}