Reverse engineering of feedforward cortical-Hippocampal microcircuits for modelling neural network function and dysfunction.
Adult neural networks
Alzheimer’s model
Electrophysiology
In vitro
MEA
Microfluidic device
Journal
Scientific reports
ISSN: 2045-2322
Titre abrégé: Sci Rep
Pays: England
ID NLM: 101563288
Informations de publication
Date de publication:
29 10 2024
29 10 2024
Historique:
received:
05
04
2024
accepted:
21
10
2024
medline:
30
10
2024
pubmed:
30
10
2024
entrez:
30
10
2024
Statut:
epublish
Résumé
Engineered biological neural networks are indispensable models for investigation of neural function and dysfunction from the subcellular to the network level. Notably, advanced neuroengineering approaches are of significant interest for their potential to replicate the topological and functional organization of brain networks. In this study, we reverse engineered feedforward neural networks of primary cortical and hippocampal neurons, using a custom-designed multinodal microfluidic device with Tesla valve inspired microtunnels. By interfacing this device with nanoporous microelectrodes, we show that the reverse engineered multinodal neural networks exhibit capacity for both segregated and integrated functional activity, mimicking brain network dynamics. To advocate the broader applicability of our model system, we induced localized perturbations with amyloid beta to study the impact of pathology on network functionality. Additionally, we demonstrate long-term culturing of subregion- and layer specific neurons extracted from the entorhinal cortex and hippocampus of adult Alzheimer's-model mice and rats. Our results thus highlight the potential of our approach for reverse engineering of anatomically relevant multinodal neural networks to study dynamic structure-function relationships in both healthy and pathological conditions.
Identifiants
pubmed: 39472479
doi: 10.1038/s41598-024-77157-4
pii: 10.1038/s41598-024-77157-4
doi:
Substances chimiques
Amyloid beta-Peptides
0
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
26021Informations de copyright
© 2024. The Author(s).
Références
Wagenaar, D. A., Pine, J. & Potter, S. M. An extremely rich repertoire of bursting patterns during the development of cortical cultures. BMC Neurosci. 7, 11. https://doi.org/10.1186/1471-2202-7-11 (2006).
doi: 10.1186/1471-2202-7-11
pubmed: 16464257
pmcid: 1420316
Chiappalone, M., Bove, M., Vato, A., Tedesco, M. & Martinoia, S. Dissociated cortical networks show spontaneously correlated activity patterns during in vitro development. Brain Res. 1093(1), 41–53. https://doi.org/10.1016/j.brainres.2006.03.049 (2006).
doi: 10.1016/j.brainres.2006.03.049
pubmed: 16712817
Heiney, K. et al. Neuronal avalanche dynamics and functional connectivity elucidate information propagation in vitro. Front. Neural Circuits 16, 980631. https://doi.org/10.3389/fncir.2022.980631 (2022).
doi: 10.3389/fncir.2022.980631
pubmed: 36188125
pmcid: 9520060
Poli, D., Wheeler, B. C., DeMarse, T. B. & Brewer, G. J. Pattern separation and completion of distinct axonal inputs transmitted via micro-tunnels between co-cultured hippocampal dentate, ca3, ca1 and entorhinal cortex networks. J. Neural Eng. 15(4), 046009. https://doi.org/10.1088/1741-2552/aabc20 (2018).
doi: 10.1088/1741-2552/aabc20
pubmed: 29623900
pmcid: 6021217
Taylor, A. M. et al. Microfluidic multicompartment device for neuroscience research. Langmuir 19(5), 1551–56. https://doi.org/10.1021/la026417v (2003).
doi: 10.1021/la026417v
pubmed: 20725530
pmcid: 2923462
Taylor, A. M. et al. A microfluidic culture platform for cns axonal injury, regeneration and transport. Nat. Methods 2, 599–605. https://doi.org/10.1038/nmeth777 (2005).
doi: 10.1038/nmeth777
pubmed: 16094385
pmcid: 1558906
Pan, L. et al. An in vitro method to manipulate the direction and functional strength between neural populations. Front. Neural Circuits[SPACE] https://doi.org/10.3389/fncir.2015.00032 (2015).
doi: 10.3389/fncir.2015.00032
pubmed: 26793069
pmcid: 4511845
Brofiga, M., Pisano, M., Tedesco, M., Boccaccio, A. & Massobrio, P. Functional inhibitory connections modulate the electrophysiological activity patterns of cortical-hippocampal ensemble. Cereb. Cortex 32(9), 1866–1881. https://doi.org/10.1093/cercor/bhab318 (2022).
doi: 10.1093/cercor/bhab318
pubmed: 34535794
Obien, M. E. J., Deligkaris, K., Bullmann, T., Bakkum, D. J. & Frey, U. Revealing neuronal function through microelectrode array recordings. Front Neurosci.[SPACE] https://doi.org/10.3389/fnins.2014.00423 (2015).
doi: 10.3389/fnins.2014.00423
pubmed: 25610364
pmcid: 4285113
Mossink, B. et al. Human neuronal networks on micro-electrode arrays are a highly robust tool to study disease-specific genotype-phenotype correlations in vitro. Stem Cell Rep. 16(9), 2182–2196. https://doi.org/10.1016/j.stemcr.2021.07.001 (2021).
doi: 10.1016/j.stemcr.2021.07.001
Keller, J. M. & Frega, M. Past, present, and future of neuronal models in vitro. Adv Neurobiol. 22, 3–17. https://doi.org/10.1007/978-3-030-11135-9_1 (2019).
doi: 10.1007/978-3-030-11135-9_1
pubmed: 31073930
DeMarse, T. B., Pan, L., Alagapan, S., Brewer, G. J. & Wheeler, B. C. Feed-forward propagation of temporal and rate information between cortical populations during coherent activation in engineered in vitro networks. Front. Neural Circuits[SPACE] https://doi.org/10.3389/fncir.2016.00032 (2016).
doi: 10.3389/fncir.2016.00032
pubmed: 27445701
pmcid: 4923256
Benito, N. et al. Spatial modules of coherent activity in pathway-specific lfps in the hippocampus reflect topology and different modes of presynaptic synchronization. Cereb. Cortex 24(7), 1738–52. https://doi.org/10.1093/cercor/bht022 (2014).
doi: 10.1093/cercor/bht022
pubmed: 23395845
Withers, G. S., James, C. D., Kingman, C. E., Craighead, H. G. & Banker, G. A. Effects of substrate geometry on growth cone behavior and axon branching. J. Neurobiol. 66(11), 1183–94. https://doi.org/10.1002/neu.20298 (2006).
doi: 10.1002/neu.20298
pubmed: 16858695
Dent, E. W., Gupton, S. L. & Gertler, F. B. The growth cone cytoskeleton in axon outgrowth and guidance. Cold Spring Harb. Perspect. Biol. 3(3), a001800. https://doi.org/10.1101/cshperspect.a001800 (2011).
doi: 10.1101/cshperspect.a001800
pubmed: 21106647
pmcid: 3039926
Gangatharan, G., Schneider-Maunoury, S. & Breau, M. A. Role of mechanical cues in shaping neuronal morphology and connectivity. Biol. Cell 110(6), 125–36. https://doi.org/10.1111/boc.201800003 (2018).
doi: 10.1111/boc.201800003
pubmed: 29698566
Peyrin, J. M. et al. Axon diodes for the reconstruction of oriented neuronal networks in microfluidic chambers. Lab Chip 11(21), 3663–73. https://doi.org/10.1039/c1lc20014c (2011).
doi: 10.1039/c1lc20014c
pubmed: 21922081
Malishev, E. et al. Microfluidic device for unidirectional axon growth. J. Phys. Conf. Ser. 643, 012025. https://doi.org/10.1088/1742-6596/643/1/012025 (2015).
doi: 10.1088/1742-6596/643/1/012025
le Feber, J., Postma, W., de Weerd, E., Weusthof, M. & Rutten, W. L. Barbed channels enhance unidirectional connectivity between neuronal networks cultured on multi electrode arrays. Front. Neurosci. 9, 412. https://doi.org/10.3389/fnins.2015.00412 (2015).
doi: 10.3389/fnins.2015.00412
pubmed: 26578869
pmcid: 4630305
Gladkov, A. et al. Design of cultured neuron networks in vitro with predefined connectivity using asymmetric microfluidic channels. Sci. Rep. 7(1), 15625. https://doi.org/10.1038/s41598-017-15506-2 (2017).
doi: 10.1038/s41598-017-15506-2
pubmed: 29142321
pmcid: 5688062
Na, S. et al. Microfluidic neural axon diode. Technology 4(4), 240–8. https://doi.org/10.1142/S2339547816500102 (2016).
doi: 10.1142/S2339547816500102
Holloway, P. M. et al. Asymmetric confinement for defining outgrowth directionality. Lab Chip 19(8), 1484–89. https://doi.org/10.1039/c9lc00078j (2019).
doi: 10.1039/c9lc00078j
pubmed: 30899932
Renault, R., Durand, J.-B., Viovy, J.-L. & Villard, C. Asymmetric axonal edge guidance: a new paradigm for building oriented neuronal networks. Lab Chip 16(12), 2188–91. https://doi.org/10.1039/c6lc00479b (2016).
doi: 10.1039/c6lc00479b
pubmed: 27225661
Winter-Hjelm, N., Tomren, Å. B., Sikorski, P., Sandvig, A. & Sandvig, I. Structure-function dynamics of engineered, modular neuronal networks with controllable afferent-efferent connectivity. J. Neural Eng. 20, 046024. https://doi.org/10.1088/1741-2552/ace37f (2023).
doi: 10.1088/1741-2552/ace37f
Vakilna, Y. S., Tang, W. C., Wheeler, B. C. & Brewer, G. J. The flow of axonal information among hippocampal subregions: 1. Feed-forward and feedback network spatial dynamics underpinning emergent information processing. Front. Neural Circuits 15, 660837. https://doi.org/10.3389/fncir.2021.660837 (2021).
doi: 10.3389/fncir.2021.660837
pubmed: 34512275
pmcid: 8430040
Yamamoto, H. et al. Impact of modular organization on dynamical richness in cortical networks. Sci. Adv. 4, 11. https://doi.org/10.1126/sciadv.aau4914 (2018).
doi: 10.1126/sciadv.aau4914
Park, M. U. et al. Collective dynamics of neuronal activities in various modular networks. Lab Chip 21(5), 951–61. https://doi.org/10.1039/d0lc01106a (2021).
doi: 10.1039/d0lc01106a
pubmed: 33475100
van de Wijdeven, R. et al. Structuring a multi-nodal neural network in vitro within a novel design microfluidic chip. Biomed. Microdevices 20, 9. https://doi.org/10.1007/s10544-017-0254-4 (2018).
doi: 10.1007/s10544-017-0254-4
pubmed: 29294210
Dworak, B. J. & Wheeler, B. C. Novel mea platform with pdms microtunnels enables the detection of action potential propagation from isolated axons in culture. Lab Chip 9(3), 404–10. https://doi.org/10.1039/b806689b (2009).
doi: 10.1039/b806689b
pubmed: 19156289
Levy, O., Ziv, N. E. & Marom, S. Enhancement of neural representation capacity by modular architecture in networks of cortical neurons. Eur. J. Neurosci. 35(11), 1753–60. https://doi.org/10.1111/j.1460-9568.2012.08094.x (2012).
doi: 10.1111/j.1460-9568.2012.08094.x
pubmed: 22507055
Baruchi, I., Volman, V., Raichman, N., Shein, M. & Ben-Jacob, E. The emergence and properties of mutual synchronization in in vitro coupled cortical networks. Eur. J. Neurosci. 28(9), 1825–35. https://doi.org/10.1111/j.1460-9568.2008.06487.x (2008).
doi: 10.1111/j.1460-9568.2008.06487.x
pubmed: 18973597
Shein-Idelson, M., Cohen, G., Ben-Jacob, E. & Hanein, Y. Modularity induced gating and delays in neuronal networks. PLoS Comput. Biol. 12(4), e1004883. https://doi.org/10.1371/journal.pcbi.1004883 (2016).
doi: 10.1371/journal.pcbi.1004883
pubmed: 27104350
pmcid: 4841573
van Strien, N. M., Cappaert, N. L. & Witter, M. P. The anatomy of memory: An interactive overview of the parahippocampal-hippocampal network. Nat. Rev. Neurosci. 10(4), 272–82. https://doi.org/10.1038/nrn2614 (2009).
doi: 10.1038/nrn2614
pubmed: 19300446
Braak, H. & Braak, E. Neuropathological stageing of Alzheimer-related changes. Acta Neuropathol. 82(4), 239–59. https://doi.org/10.1007/bf00308809 (1991).
doi: 10.1007/bf00308809
pubmed: 1759558
Thal, D. R., Rüb, U., Orantes, M. & Braak, H. Phases of a beta-deposition in the human brain and its relevance for the development of ad. Neurology 58(12), 1791–800. https://doi.org/10.1212/wnl.58.12.1791 (2002).
doi: 10.1212/wnl.58.12.1791
pubmed: 12084879
Vossel, K. A. et al. Seizures and epileptiform activity in the early stages of Alzheimer disease. JAMA Neurol. 70(9), 1158–66. https://doi.org/10.1001/jamaneurol.2013.136 (2013).
doi: 10.1001/jamaneurol.2013.136
pubmed: 23835471
pmcid: 4013391
Gómez-Isla, T. et al. Profound loss of layer ii entorhinal cortex neurons occurs in very mild Alzheimer’s disease. J. Neurosci. 16(14), 4491–500. https://doi.org/10.1523/JNEUROSCI.16-14-04491.1996 (1996).
doi: 10.1523/JNEUROSCI.16-14-04491.1996
pubmed: 8699259
pmcid: 6578866
Kordower, J. H. et al. Loss and atrophy of layer ii entorhinal cortex neurons in elderly people with mild cognitive impairment. Ann. Neurol. 49(2), 202–13 (2001).
doi: 10.1002/1531-8249(20010201)49:2<202::AID-ANA40>3.0.CO;2-3
pubmed: 11220740
Scheff, S. W., Price, D. A., Schmitt, F. A. & Mufson, E. J. Hippocampal synaptic loss in early Alzheimer’s disease and mild cognitive impairment. Neurobiol. Aging 27(10), 1372–84. https://doi.org/10.1016/j.neurobiolaging.2005.09.012 (2006).
doi: 10.1016/j.neurobiolaging.2005.09.012
pubmed: 16289476
Scheff, S. W., Price, D. A., Schmitt, F. A., DeKosky, S. T. & Mufson, E. J. Synaptic alterations in ca1 in mild Alzheimer disease and mild cognitive impairment. Neurology 68(18), 1501–8. https://doi.org/10.1212/01.wnl.0000260698.46517.8f (2007).
doi: 10.1212/01.wnl.0000260698.46517.8f
pubmed: 17470753
Dong, Y., Sameni, S., Digman, M. A. & Brewer, G. J. Reversibility of age-related oxidized free nadh redox states in alzheimer’s disease neurons by imposed external cys/cyss redox shifts. Sci. Rep. 9(1), 11274. https://doi.org/10.1038/s41598-019-47582-x (2019).
doi: 10.1038/s41598-019-47582-x
pubmed: 31375701
pmcid: 6677822
Dong, Y., Digman, M. A. & Brewer, G. J. Age- and ad-related redox state of nadh in subcellular compartments by fluorescence lifetime imaging microscopy. Geroscience 41(1), 51–67. https://doi.org/10.1007/s11357-019-00052-8 (2019).
doi: 10.1007/s11357-019-00052-8
pubmed: 30729413
pmcid: 6423217
Hanssen, K. S., Witter, M. P., Sandvig, A. I. & Kobro-Flatmoen, A. Dissection and culturing of adult lateral entorhinal cortex layer ii neurons from app/ps1 alzheimer model mice. J. Neurosci. Methods 390, 109840. https://doi.org/10.1016/j.jneumeth.2023.109840 (2023).
doi: 10.1016/j.jneumeth.2023.109840
pubmed: 36948358
Pasquale, V., Martinoia, S. & Chiappalone, M. A self-adapting approach for the detection of bursts and network bursts in neuronal cultures. J. Comput. Neurosci. 29(1–2), 213–29. https://doi.org/10.1007/s10827-009-0175-1 (2010).
doi: 10.1007/s10827-009-0175-1
pubmed: 19669401
Weir, J. S., Christiansen, N., Sandvig, A. & Sandvig, I. Selective inhibition of excitatory synaptic transmission alters the emergent bursting dynamics of in vitro neural networks. Front. Neural Circuits 17, 1020487. https://doi.org/10.3389/fncir.2023.1020487 (2023).
doi: 10.3389/fncir.2023.1020487
pubmed: 36874945
pmcid: 9978115
Nunez, J. Differential expression of microtubule components during brain development. Dev. Neurosci. 8(3), 125–41. https://doi.org/10.1159/000112248 (1986).
doi: 10.1159/000112248
pubmed: 3533503
Johnson, G. V. W. & Jope, R. S. The role of microtubule-associated protein 2 (map-2) in neuronal growth, plasticity, and degeneration. J. Neurosci. Res. 33(4), 505–12. https://doi.org/10.1002/jnr.490330402 (1992).
doi: 10.1002/jnr.490330402
pubmed: 1484385
Tischfield, M. A. et al. Human tubb3 mutations perturb microtubule dynamics, kinesin interactions, and axon guidance. Cell 140(1), 74–87. https://doi.org/10.1016/j.cell.2009.12.011 (2010).
doi: 10.1016/j.cell.2009.12.011
pubmed: 20074521
pmcid: 3164117
Oestreicher, A. B., De Graan, P. N. E., Gispen, W. H., Verhaagen, J. & Schrama, L. H. B-50, the growth associated protein-43: modulation of cell morphology and communication in the nervous system. Prog. Neurobiol. 53(6), 627–86. https://doi.org/10.1016/S0301-0082(97)00043-9 (1997).
doi: 10.1016/S0301-0082(97)00043-9
pubmed: 9447616
Mullen, R. J., Buck, C. R. & Smith, A. M. Neun, a neuronal specific nuclear protein in vertebrates. Development 116(1), 201–11. https://doi.org/10.1242/dev.116.1.201 (1992).
doi: 10.1242/dev.116.1.201
pubmed: 1483388
Schlaepfer, W. W. & Bruce, J. Simultaneous up-regulation of neurofilament proteins during the postnatal development of the rat nervous system. J. Neurosci. Res. 25(1), 39–49. https://doi.org/10.1002/jnr.490250106 (1990).
doi: 10.1002/jnr.490250106
pubmed: 2108255
Eng, L. F., Ghirnikar, R. S. & Lee, Y. L. Glial fibrillary acidic protein: Gfap-thirty-one years (1969–2000). Neurochem. Res. 25(9–10), 1439–51. https://doi.org/10.1023/A:1007677003387 (2000).
doi: 10.1023/A:1007677003387
pubmed: 11059815
Wiedenmann, B. & Franke, W. W. Identification and localization of synaptophysin, an integral membrane glycoprotein of mr 38,000 characteristic of presynaptic vesicles. Cell 41(3), 1017–28. https://doi.org/10.1016/S0092-8674(85)80082-9 (1985).
doi: 10.1016/S0092-8674(85)80082-9
pubmed: 3924408
Cho, K.-O., Hunt, C. A. & Kennedy, M. B. The rat brain postsynaptic density fraction contains a homolog of the drosophila discs-large tumor suppressor protein. Neuron 9(5), 929–42. https://doi.org/10.1016/0896-6273(92)90245-9 (1992).
doi: 10.1016/0896-6273(92)90245-9
pubmed: 1419001
Liu, X. B. & Jones, E. G. Localization of alpha type ii calcium calmodulin-dependent protein kinase at glutamatergic but not gamma-aminobutyric acid (gabaergic) synapses in thalamus and cerebral cortex. PNAS 93(14), 7332–6. https://doi.org/10.1073/pnas.93.14.7332 (1996).
doi: 10.1073/pnas.93.14.7332
pubmed: 8692993
pmcid: 38984
Erlander, M. G., Tillakaratne, N. J. K., Feldblum, S., Patel, N. & Tobin, A. J. Two genes encode distinct glutamate decarboxylases. Neuron 7(1), 91–100. https://doi.org/10.1016/0896-6273(91)90077-D (1991).
doi: 10.1016/0896-6273(91)90077-D
pubmed: 2069816
Feldblum, S., Erlander, M. G. & Tobin, A. J. Different distributions of gad65 and gad67 mrnas suggest that the two glutamate decarboxylases play distinctive functional roles. J. Neurosci. Res. 34(6), 689–706. https://doi.org/10.1002/jnr.490340612 (1993).
doi: 10.1002/jnr.490340612
pubmed: 8315667
Luján, R., Shigemoto, R. & López-Bendito, G. Glutamate and gaba receptor signalling in the developing brain. Neuroscience 130(3), 567–80. https://doi.org/10.1016/j.neuroscience.2004.09.042 (2005).
doi: 10.1016/j.neuroscience.2004.09.042
pubmed: 15590141
Luhmann, H. J., Fukuda, A. & Kilb, W. Control of cortical neuronal migration by glutamate and gaba. Front. Cell. Neurosci.[SPACE] https://doi.org/10.3389/fncel.2015.00004 (2015).
doi: 10.3389/fncel.2015.00004
pubmed: 26379499
pmcid: 4548153
Behuet, S. et al. Developmental changes of glutamate and gaba receptor densities in wistar rats. Front. neuroanat. 13, 100. https://doi.org/10.3389/fnana.2019.00100 (2019).
doi: 10.3389/fnana.2019.00100
pubmed: 31920569
pmcid: 6933313
Martínez–Cerdeño, V., Galazo, M. . J. & Clascá, F. Reelin-immunoreactive neurons, axons, and neuropil in the adult ferret brain: Evidence for axonal secretion of reelin in long axonal pathways. J. Comp. Neurol. 463(1), 92–116. https://doi.org/10.1002/cne.10748 (2003).
doi: 10.1002/cne.10748
pubmed: 12811805
Shanahan, M. Dynamical complexity in small-world networks of spiking neurons. Phys. Rev. E Stat. Nonlin. Soft Matter Phys.[SPACE] https://doi.org/10.1103/PhysRevE.78.041924 (2008).
doi: 10.1103/PhysRevE.78.041924
pubmed: 18999472
Rubinov, M., Sporns, O., Thivierge, J.-P. & Breakspear, M. Neurobiologically realistic determinants of self-organized criticality in networks of spiking neurons. PLoS Comput. Biol. 7(6), e1002038. https://doi.org/10.1371/journal.pcbi.1002038 (2011).
doi: 10.1371/journal.pcbi.1002038
pubmed: 21673863
pmcid: 3107249
Meunier, D., Lambiotte, R. & Bullmore, E. T. Modular and hierarchically modular organization of brain networks. Front. Neurosci. 4, 200. https://doi.org/10.3389/fnins.2010.00200 (2010).
doi: 10.3389/fnins.2010.00200
pubmed: 21151783
pmcid: 3000003
Busche, M. A. et al. Critical role of soluble amyloid-Î2 for early hippocampal hyperactivity in a mouse model of Alzheimer’s disease. PNAS 109(22), 8740–5. https://doi.org/10.1073/pnas.1206171109 (2012).
doi: 10.1073/pnas.1206171109
pubmed: 22592800
pmcid: 3365221
Klupp, E. et al. In alzheimer’s disease, hypometabolism in low-amyloid brain regions may be a functional consequence of pathologies in connected brain regions. Brain Connect. 4(5), 371–83. https://doi.org/10.1089/brain.2013.0212 (2014).
doi: 10.1089/brain.2013.0212
pubmed: 24870443
Schultz, A. P. et al. Phases of hyperconnectivity and hypoconnectivity in the default mode and salience networks track with amyloid and tau in clinically normal individuals. J. Neurosci. 37(16), 4323–4331. https://doi.org/10.1523/JNEUROSCI.3263-16.2017 (2017).
doi: 10.1523/JNEUROSCI.3263-16.2017
pubmed: 28314821
pmcid: 5413178
Valderhaug, V. D. et al. Early functional changes associated with alpha-synuclein proteinopathy in engineered human neural networks. Am. J. Physiol. Cell Physiol. 320, C1141-52. https://doi.org/10.1152/ajpcell.00413.2020 (2021).
doi: 10.1152/ajpcell.00413.2020
pubmed: 33950697
Fiskum, V., Winter-Hjelm, N., Christiansen, N., Sandvig, A. & Sandvig, I. Als patient-derived motor neuron networks exhibit microscale dysfunction and mesoscale compensation rendering them highly vulnerable to perturbation. bioRxiv[SPACE] https://doi.org/10.1101/2024.01.04.574167 (2024).
doi: 10.1101/2024.01.04.574167
Faust, T. E., Gunner, G. & Schafer, D. P. Mechanisms governing activity-dependent synaptic pruning in the developing mammalian cns. Nat. Rev. Neurosci. 22, 657–673. https://doi.org/10.1038/s41583-021-00507-y (2021).
doi: 10.1038/s41583-021-00507-y
van Niekerk, E. A. et al. Methods for culturing adult cns neurons reveal a cns conditioning effect. Cell Rep. Methods 2(7), 100255. https://doi.org/10.1016/j.crmeth.2022.100255 (2022).
doi: 10.1016/j.crmeth.2022.100255
pubmed: 35880023
pmcid: 9308166
Evans, M. S., Collings, M. A. & Brewer, G. J. Electrophysiology of embryonic, adult and aged rat hippocampal neurons in serum-free culture. J. Neurosci. Methods 79(1), 37–46. https://doi.org/10.1016/s0165-0270(97)00159-3 (1998).
doi: 10.1016/s0165-0270(97)00159-3
pubmed: 9531458
Varghese, K. et al. Regeneration and characterization of adult mouse hippocampal neurons in a defined in vitro system. J. Neurosci. Methods 177(1), 51–9. https://doi.org/10.1016/j.jneumeth.2008.09.022 (2009).
doi: 10.1016/j.jneumeth.2008.09.022
pubmed: 18955083
Nilssen, E. S. et al. Inhibitory connectivity dominates the fan cell network in layer ii of lateral entorhinal cortex. J. Neurosci. 38(45), 9712–27. https://doi.org/10.1523/jneurosci.1290-18.2018 (2018).
doi: 10.1523/jneurosci.1290-18.2018
pubmed: 30249791
pmcid: 6595991
Valeeva, G. et al. Emergence of coordinated activity in the developing entorhinal–hippocampal network. Cereb. Cortex 29(2), 906–920. https://doi.org/10.1093/cercor/bhy309 (2018).
doi: 10.1093/cercor/bhy309
pmcid: 6319314
Griguoli, M. & Cherubini, E. Early correlated network activity in the hippocampus: Its putative role in shaping neuronal circuits. Front. Cell. Neurosci. 11, 255. https://doi.org/10.3389/fncel.2017.00255 (2017).
doi: 10.3389/fncel.2017.00255
Canto, C. B. & Witter, M. P. Cellular properties of principal neurons in the rat entorhinal cortex. I. The lateral entorhinal cortex. Hippocampus 22(6), 1256–76. https://doi.org/10.1002/hipo.20997 (2012).
doi: 10.1002/hipo.20997
pubmed: 22162008
Shao, L.-R. & Dudek, F. E. Enhanced burst discharges in the ca1 area of the immature versus adult hippocampus: patterns and cellular mechanisms. J. Neurophysiol. 128(6), 1566–1577. https://doi.org/10.1152/jn.00327.2022 (2022).
doi: 10.1152/jn.00327.2022
pubmed: 36382903
pmcid: 9744639
Nedaei, H. et al. The calcium-free form of atorvastatin inhibits amyloid-β42) aggregation in vitro. J. Biol. Chem. 298(3), 101662. https://doi.org/10.1016/j.jbc.2022.101662 (2022).
doi: 10.1016/j.jbc.2022.101662
pubmed: 35104501
pmcid: 8898965
Percie du Sert, N. et al. The arrive guidelines 2.0: Updated guidelines for reporting animal research. PLoS Biol. 18(7), e3000410. https://doi.org/10.1371/journal.pbio.3000410 (2020).
doi: 10.1371/journal.pbio.3000410
pubmed: 32663219
pmcid: 7360023
Radde, R. et al. Abeta42-driven cerebral amyloidosis in transgenic mice reveals early and robust pathology. EMBO Rep. 7(9), 940–6. https://doi.org/10.1038/sj.embor.7400784 (2006).
doi: 10.1038/sj.embor.7400784
pubmed: 16906128
pmcid: 1559665
Leon, W. C. et al. A novel transgenic rat model with a full alzheimer’s-like amyloid pathology displays pre-plaque intracellular amyloid-beta-associated cognitive impairment. J. Alzheimers Dis. 20(1), 113–26. https://doi.org/10.3233/jad-2010-1349 (2010).
doi: 10.3233/jad-2010-1349
pubmed: 20164597
Heggland, I., Kvello, P. & Witter, M. P. Electrophysiological characterization of networks and single cells in the hippocampal region of a transgenic rat model of alzheimer’s disease. eNeuro[SPACE] https://doi.org/10.1523/eneuro.0448-17.2019 (2019).
doi: 10.1523/eneuro.0448-17.2019
pubmed: 30809590
pmcid: 6390198
Richter, K. N. et al. Glyoxal as an alternative fixative to formaldehyde in immunostaining and super-resolution microscopy. EMBO J. 37, 139–59. https://doi.org/10.15252/embj.201695709 (2017).
doi: 10.15252/embj.201695709
pubmed: 29146773
pmcid: 5753035
Lansey, J. C. Beautiful and distinguishable line colors + colormap, (2022).
Brewer, C. A., Hatchard, G. W. & Harrower, M. A. Colorbrewer in print: A catalog of color schemes for maps. Cartogr. Geogr. Inf. Sci. 30(1), 5–32. https://doi.org/10.1559/152304003100010929 (2003).
doi: 10.1559/152304003100010929
Maccione, A. et al. A novel algorithm for precise identification of spikes in extracellularly recorded neuronal signals. J. Neurosci. Methods 177(1), 241–9. https://doi.org/10.1016/j.jneumeth.2008.09.026 (2009).
doi: 10.1016/j.jneumeth.2008.09.026
pubmed: 18957306
Kraus, B. Spike raster plot, (2022).
Bologna, L. L. et al. Investigating neuronal activity by spycode multi-channel data analyzer. Neural Netw. 23(6), 685–97. https://doi.org/10.1016/j.neunet.2010.05.002 (2010).
doi: 10.1016/j.neunet.2010.05.002
pubmed: 20554151
Wang, X.-J. Pacemaker neurons for the theta rhythm and their synchronization in the septohippocampal reciprocal loop. J. Neurophysiol. 87(2), 889–900. https://doi.org/10.1152/jn.00135.2001 (2002).
doi: 10.1152/jn.00135.2001
pubmed: 11826054
Blondel, V. D., Guillaume, J.-L., Lambiotte, R. & Lefebvre, E. Fast unfolding of communities in large networks. J. Stat. Mech. Theory Exp. 2008, P10008. https://doi.org/10.1088/1742-5468/2008/10/P10008 (2008).
doi: 10.1088/1742-5468/2008/10/P10008
Rubinov, M. & Sporns, O. Complex network measures of brain connectivity: uses and interpretations. Neuroimage 52(3), 1059–69. https://doi.org/10.1016/j.neuroimage.2009.10.003 (2010).
doi: 10.1016/j.neuroimage.2009.10.003
pubmed: 19819337
Wagenaar, D. A. & Potter, S. M. Real-time multi-channel stimulus artifact suppression by local curve fitting. J. Neurosci. Methods 120(2), 113–20. https://doi.org/10.1016/s0165-0270(02)00149-8 (2002).
doi: 10.1016/s0165-0270(02)00149-8
pubmed: 12385761