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@ARTICLE{Khanzada:280245,
      author       = {Khanzada, Shahrukh and Hu, Xin and Emery, Brett Addison and
                      Średniawa, Władysław and Wójcik, Daniel K and
                      Kempermann, Gerd and Amin, Hayder},
      title        = {{D}ynamic mapping of network-level {LTP} in the hippocampus
                      via high-resolution bioelectrical sensing.},
      journal      = {APL bioengineering},
      volume       = {9},
      number       = {3},
      issn         = {2473-2877},
      address      = {Melville, NY},
      publisher    = {AIP Publishing},
      reportid     = {DZNE-2025-00923},
      pages        = {036109},
      year         = {2025},
      abstract     = {Understanding the complexity of neural network dynamics
                      demands advanced biosensing technologies capable of
                      capturing large-scale interactions with high spatial and
                      temporal precision. Traditional approaches, such as
                      patch-clamp and field recordings, are inherently limited in
                      resolving network-wide synaptic connections, particularly
                      long-term potentiation (LTP), due to their localized scope
                      and indirect access to hippocampal subfields. To address
                      these challenges, we introduce EvoNES, a CMOS-based
                      high-definition 4096 microelectrode array platform that
                      leverages bidirectional stimulus-responsive biosensing
                      functionality. By coupling precise external electrode
                      stimulation targeting the Schaffer collateral and medial
                      perforant pathways with simultaneous on-chip bioelectrical
                      recordings, EvoNES enables the first real-time
                      quantification of evoked responses and LTP dynamics across
                      the entire hippocampal circuit. This system bridges critical
                      gaps in traditional techniques, providing a mesoscopic-scale
                      view of cell assemblies interplay and delivering
                      unprecedented insights into the distributed mechanisms
                      underlying memory encoding and learning processes. Advanced
                      computational analyses generate variation maps revealing
                      distinct voltage fluctuation patterns and differential
                      sensitivity across hippocampal subregions during synaptic
                      potentiation. Our findings identify four distinct waveform
                      classes within the CA1-CA3 network and three unique evoked
                      firing patterns in the dentate gyrus (DG). Post-tetanic
                      responses show faster induction, expanded activated zones,
                      and the activation of previously silent cell assemblies,
                      indicating significant network restructuring. Applied in
                      aged mice, EvoNES demonstrates age-dependent changes in
                      network LTP, both quantitatively and qualitatively. This
                      high-resolution biosensing platform in a live neural context
                      provides unprecedented insights into hippocampal memory
                      formation and offers a powerful tool for investigating
                      neural plasticity and network interactions in both health
                      and disease states.},
      cin          = {AG Amin / AG Kempermann},
      ddc          = {570},
      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:40740280},
      pmc          = {pmc:PMC12310272},
      doi          = {10.1063/5.0258985},
      url          = {https://pub.dzne.de/record/280245},
}