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Gene-expression correlates of the oscillatory signatures supporting human episodic memory encoding

Nature Neuroscience volume 24, pages 554–564 (2021)Cite this article

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Abstract

In humans, brain oscillations support critical features of memory formation. However, understanding the molecular mechanisms underlying this activity remains a major challenge. Here, we measured memory-sensitive oscillations using intracranial electroencephalography recordings from the temporal cortex of patients performing an episodic memory task. When these patients subsequently underwent resection, we employed transcriptomics on the temporal cortex to link gene expression with brain oscillations and identified genes correlated with oscillatory signatures of memory formation across six frequency bands. A co-expression analysis isolated oscillatory signature-specific modules associated with neuropsychiatric disorders and ion channel activity, with highly correlated genes exhibiting strong connectivity within these modules. Using single-nucleus transcriptomics, we further revealed that these modules are enriched for specific classes of both excitatory and inhibitory neurons, and immunohistochemistry confirmed expression of highly correlated genes. This unprecedented dataset of patient-specific brain oscillations coupled to genomics unlocks new insights into the genetic mechanisms that support memory encoding.

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Fig. 1: Within-individual study design and quality control.
Fig. 2: Genes associated with SMEs are distinct.
Fig. 3: Gene co-expression networks highlight cellular processes implicated in memory encoding.
Fig. 4: SME-specific modules capture genes dysregulated in neuropsychiatric disorders.
Fig. 5: SME-specific modules are enriched for excitatory and inhibitory neurons.
Fig. 6: snATAC-seq highlights TFs regulating SME-correlated modules.

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Data availability

The RNA-seq dataset used for memory oscillatory signature analysis in this study are available at GEO with accession number GSE139914.

Code availability

Custom R codes for the quality control, MVA, correlative analysis, permutation/bootstraps, WGCNA, snRNA-seq analysis, snATAC-seq analysis, visualizations, functional enrichments, GWAS enrichment and gene set enrichments are available at https://github.com/konopkalab/Within_Subject.

References

  1. Morgan, S. E. et al. Cortical patterning of abnormal morphometric similarity in psychosis is associated with brain expression of schizophrenia-related genes. Proc. Natl Acad. Sci. USA 116, 9604–9609 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  2. Lombardo, M. V. et al. Large-scale associations between the leukocyte transcriptome and BOLD responses to speech differ in autism early language outcome subtypes. Nat. Neurosci. 21, 1680–1688 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. Romero-Garcia, R., Warrier, V., Bullmore, E. T., Baron-Cohen, S. & Bethlehem, R. A. I. Synaptic and transcriptionally downregulated genes are associated with cortical thickness differences in autism. Mol. Psychiatry 24, 1053–1064 (2019).

    Article  CAS  PubMed  Google Scholar 

  4. Wang, G. Z. et al. Correspondence between resting-state activity and brain gene expression. Neuron 88, 659–666 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Patania, A. et al. Topological gene expression networks recapitulate brain anatomy and function. Netw. Neurosci. 3, 744–762 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  6. Le, B. D. & Stein, J. L. Mapping causal pathways from genetics to neuropsychiatric disorders using genome-wide imaging genetics: current status and future directions. Psychiatry Clin. Neurosci. 73, 357–369 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  7. Konopka, G. Cognitive genomics: linking genes to behavior in the human brain. Netw. Neurosci. 1, 3–13 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  8. Berto, S., Wang, G. Z., Germi, J., Lega, B. C. & Konopka, G. Human genomic signatures of brain oscillations during memory encoding. Cereb. Cortex 28, 1733–1748 (2018).

    Article  PubMed  Google Scholar 

  9. Long, N. M., Burke, J. F. & Kahana, M. J. Subsequent memory effect in intracranial and scalp EEG. NeuroImage 84, 488–494 (2014).

    Article  PubMed  Google Scholar 

  10. Mukamel, R. & Fried, I. Human intracranial recordings and cognitive neuroscience. Annu. Rev. Psychol. 63, 511–537 (2012).

    Article  PubMed  Google Scholar 

  11. Sederberg, P. B. et al. Hippocampal and neocortical gamma oscillations predict memory formation in humans. Cereb. Cortex 17, 1190–1196 (2007).

    Article  PubMed  Google Scholar 

  12. Nakamura, K. & Kubota, K. The primate temporal pole: its putative role in object recognition and memory. Behav. Brain Res. 77, 53–77 (1996).

    Article  CAS  PubMed  Google Scholar 

  13. Hill, P. F., King, D. R., Lega, B. C. & Rugg, M. D. Comparison of fMRI correlates of successful episodic memory encoding in temporal lobe epilepsy patients and healthy controls. NeuroImage 207, 116397 (2020).

    Article  PubMed  Google Scholar 

  14. Sederberg, P. B., Howard, M. W. & Kahana, M. J. A context-based theory of recency and contiguity in free recall. Psychol. Rev. 115, 893–912 (2008).

    Article  PubMed  PubMed Central  Google Scholar 

  15. Arora, A. et al. Comparison of logistic regression, support vector machines, and deep learning classifiers for predicting memory encoding success using human intracranial EEG recordings. J. Neural Eng. 15, 066028 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  16. Lin, J. J. et al. Theta band power increases in the posterior hippocampus predict successful episodic memory encoding in humans. Hippocampus 27, 1040–1053 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Sederberg, P. B., Kahana, M. J., Howard, M. W., Donner, E. J. & Madsen, J. R. Theta and gamma oscillations during encoding predict subsequent recall. J. Neurosci. 23, 10809–10814 (2003).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Langfelder, P. & Horvath, S. WGCNA: an R package for weighted correlation network analysis. BMC Bioinformatics 9, 559 (2008).

    Article  PubMed  PubMed Central  Google Scholar 

  19. Berkel, S. et al. Mutations in the SHANK2 synaptic scaffolding gene in autism spectrum disorder and mental retardation. Nat. Genet. 42, 489–491 (2010).

    Article  CAS  PubMed  Google Scholar 

  20. Peykov, S. et al. Rare SHANK2 variants in schizophrenia. Mol. Psychiatry 20, 1487–1488 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Won, H. et al. Autistic-like social behaviour in Shank2-mutant mice improved by restoring NMDA receptor function. Nature 486, 261–265 (2012).

    Article  CAS  PubMed  Google Scholar 

  22. Gandal, M. J. et al. Transcriptome-wide isoform-level dysregulation in ASD, schizophrenia, and bipolar disorder. Science 362, eaat8127 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Dembrow, N. C., Zemelman, B. V. & Johnston, D. Temporal dynamics of L5 dendrites in medial prefrontal cortex regulate integration versus coincidence detection of afferent inputs. J. Neurosci. 35, 4501–4514 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Silva, L. R., Amitai, Y. & Connors, B. W. Intrinsic oscillations of neocortex generated by layer 5 pyramidal neurons. Science 251, 432–435 (1991).

    Article  CAS  PubMed  Google Scholar 

  25. Kim, E. J., Juavinett, A. L., Kyubwa, E. M., Jacobs, M. W. & Callaway, E. M. Three types of cortical layer 5 neurons that differ in brain-wide connectivity and function. Neuron 88, 1253–1267 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Xia, F. et al. Parvalbumin-positive interneurons mediate neocortical-hippocampal interactions that are necessary for memory consolidation. eLife https://doi.org/10.7554/eLife.27868 (2017).

  27. Naka, A. et al. Complementary networks of cortical somatostatin interneurons enforce layer specific control. eLife https://doi.org/10.7554/eLife.43696 (2019).

  28. Veit, J., Hakim, R., Jadi, M. P., Sejnowski, T. J. & Adesnik, H. Cortical gamma band synchronization through somatostatin interneurons. Nat. Neurosci. 20, 951–959 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Pi, H. J. et al. Cortical interneurons that specialize in disinhibitory control. Nature 503, 521–524 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Lee, S., Kruglikov, I., Huang, Z. J., Fishell, G. & Rudy, B. A disinhibitory circuit mediates motor integration in the somatosensory cortex. Nat. Neurosci. 16, 1662–1670 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Kamigaki, T. & Dan, Y. Delay activity of specific prefrontal interneuron subtypes modulates memory-guided behavior. Nat. Neurosci. 20, 854–863 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Pepper, R. E., Pitman, K. A., Cullen, C. L. & Young, K. M. How do cells of the oligodendrocyte lineage affect neuronal circuits to influence motor function, memory and mood? Front. Cell. Neurosci. 12, 399 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Um, J. W. & Ko, J. LAR-RPTPs: synaptic adhesion molecules that shape synapse development. Trends Cell Biol. 23, 465–475 (2013).

    Article  CAS  PubMed  Google Scholar 

  34. Kantojarvi, K. et al. Fine mapping of Xq11.1-q21.33 and mutation screening of RPS6KA6, ZNF711, ACSL4, DLG3, and IL1RAPL2 for autism spectrum disorders (ASD). Autism Res. 4, 228–233 (2011).

    Article  PubMed  Google Scholar 

  35. Tasic, B. et al. Adult mouse cortical cell taxonomy revealed by single cell transcriptomics. Nat. Neurosci. 19, 335–346 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. Azevedo, F. A. et al. Equal numbers of neuronal and nonneuronal cells make the human brain an isometrically scaled-up primate brain. J. Comp. Neurol. 513, 532–541 (2009).

    Article  PubMed  Google Scholar 

  37. Zhang, Y. et al. Polymorphisms in human dopamine D2 receptor gene affect gene expression, splicing, and neuronal activity during working memory. Proc. Natl Acad. Sci. USA 104, 20552–20557 (2007).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Papassotiropoulos, A. et al. Common Kibra alleles are associated with human memory performance. Science 314, 475–478 (2006).

    Article  CAS  PubMed  Google Scholar 

  39. Jacobs, J. & Kahana, M. J. Direct brain recordings fuel advances in cognitive electrophysiology. Trends Cogn. Sci. 14, 162–171 (2010).

    Article  PubMed  PubMed Central  Google Scholar 

  40. Yamazaki, Y. et al. Oligodendrocytes: facilitating axonal conduction by more than myelination. Neuroscientist 16, 11–18 (2010).

    Article  PubMed  Google Scholar 

  41. Baker, A. et al. Specialized subpopulations of deep-layer pyramidal neurons in the neocortex: bridging cellular properties to functional consequences. J. Neurosci. 38, 5441–5455 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. Carracedo, L. M. et al. A neocortical delta rhythm facilitates reciprocal interlaminar interactions via nested theta rhythms. J. Neurosci. 33, 10750–10761 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. Jinno, S. et al. Neuronal diversity in GABAergic long-range projections from the hippocampus. J. Neurosci. 27, 8790–8804 (2007).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. Kim, D. et al. Distinct roles of parvalbumin- and somatostatin-expressing interneurons in working memory. Neuron 92, 902–915 (2016).

    Article  CAS  PubMed  Google Scholar 

  45. Jacobs, J. Hippocampal theta oscillations are slower in humans than in rodents: implications for models of spatial navigation and memory. Philos. Trans. R. Soc. Lond. B Biol. Sci. 369, 20130304 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  46. Yaffe, R. B. et al. Reinstatement of distributed cortical oscillations occurs with precise spatiotemporal dynamics during successful memory retrieval. Proc. Natl Acad. Sci. USA 111, 18727–18732 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  47. Rutishauser, U., Ross, I. B., Mamelak, A. N. & Schuman, E. M. Human memory strength is predicted by theta-frequency phase-locking of single neurons. Nature 464, 903–907 (2010).

    Article  CAS  PubMed  Google Scholar 

  48. Duzel, E., Penny, W. D. & Burgess, N. Brain oscillations and memory. Curr. Opin. Neurobiol. 20, 143–149 (2010).

    Article  PubMed  Google Scholar 

  49. Kahana, M. J. The cognitive correlates of human brain oscillations. J. Neurosci. 26, 1669–1672 (2006).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  50. Vaz, A. P., Inati, S. K., Brunel, N. & Zaghloul, K. A. Coupled ripple oscillations between the medial temporal lobe and neocortex retrieve human memory. Science 363, 975–978 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  51. Lega, B., Burke, J., Jacobs, J. & Kahana, M. J. Slow-theta-to-gamma phase-amplitude coupling in human hippocampus supports the formation of new episodic memories. Cereb. Cortex 26, 268–278 (2016).

    Article  PubMed  Google Scholar 

  52. Muñoz, M. D., Antolín-Vallespín, M., Tapia-González, S. & Sánchez-Capelo, A. Smad3 deficiency inhibits dentate gyrus LTP by enhancing GABA neurotransmission. J. Neurochem. 137, 190–199 (2016).

    Article  PubMed  Google Scholar 

  53. Donoghue, T. et al. Parameterizing neural power spectra. Nat. Neurosci. 23, 1655–1665 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  54. Herweg, N. A., Solomon, E. A. & Kahana, M. J. Theta oscillations in human memory. Trends Cogn. Sci. 24, 208–227 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  55. Natu, V. S. et al. Stimulation of the posterior cingulate cortex impairs episodic memory encoding. J. Neurosci. 39, 7173–7182 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  56. Goyal, A. et al. Functionally distinct high and low theta oscillations in the human hippocampus. Nat. Commun. 11, 2469 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  57. Watrous, A. J., Miller, J., Qasim, S. E., Fried, I. & Jacobs, J. Phase-tuned neuronal firing encodes human contextual representations for navigational goals. eLife https://doi.org/10.7554/eLife.32554 (2018).

  58. Fischl, B. et al. Whole brain segmentation: automated labeling of neuroanatomical structures in the human brain. Neuron 33, 341–355 (2002).

    Article  CAS  PubMed  Google Scholar 

  59. Ghose, S., Gleason, K. A., Potts, B. W., Lewis-Amezcua, K. & Tamminga, C. A. Differential expression of metabotropic glutamate receptors 2 and 3 in schizophrenia: a mechanism for antipsychotic drug action? Am. J. Psychiatry 166, 812–820 (2009).

    Article  PubMed  PubMed Central  Google Scholar 

  60. Takahashi, J. S. et al. ChIP-seq and RNA-seq methods to study circadian control of transcription in mammals. Methods Enzymol. 551, 285–321 (2015).

    Article  CAS  PubMed  Google Scholar 

  61. Habib, N. et al. Massively parallel single-nucleus RNA-seq with DroNc-seq. Nat. Methods 14, 955–958 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  62. Zheng, G. X. et al. Massively parallel digital transcriptional profiling of single cells. Nat. Commun. 8, 14049 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  63. Yeh, Y. H. et al. Transforming growth factor-beta and oxidative stress mediate tachycardia-induced cellular remodelling in cultured atrial-derived myocytes. Cardiovasc. Res. 91, 62–70 (2011).

    Article  CAS  PubMed  Google Scholar 

  64. Del Cid-Pellitero, E., Plavski, A., Mainville, L. & Jones, B. E. Homeostatic changes in GABA and glutamate receptors on excitatory cortical neurons during sleep deprivation and recovery. Front. Syst. Neurosci. 11, 17 (2017).

    PubMed  PubMed Central  Google Scholar 

  65. Dobin, A. et al. STAR: ultrafast universal RNA-seq aligner. Bioinformatics 29, 15–21 (2013).

    Article  CAS  PubMed  Google Scholar 

  66. Wang, L., Wang, S. & Li, W. RSeQC: quality control of RNA-seq experiments. Bioinformatics 28, 2184–2185 (2012).

    Article  CAS  PubMed  Google Scholar 

  67. Anders, S., Pyl, P. T. & Huber, W. HTSeq—a Python framework to work with high-throughput sequencing data. Bioinformatics 31, 166–169 (2015).

    Article  CAS  PubMed  Google Scholar 

  68. Robinson, M. D., McCarthy, D. J. & Smyth, G. K. edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics 26, 139–140 (2010).

    Article  CAS  PubMed  Google Scholar 

  69. Smith, T., Heger, A. & Sudbery, I. UMI-tools: modeling sequencing errors in unique molecular identifiers to improve quantification accuracy. Genome Res. 27, 491–499 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  70. Liao, Y., Smyth, G. K. & Shi, W. featureCounts: an efficient general purpose program for assigning sequence reads to genomic features. Bioinformatics 30, 923–930 (2014).

    Article  CAS  PubMed  Google Scholar 

  71. Stuart, T. et al. Comprehensive integration of single-cell data. Cell 177, 1888–1902.e21 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  72. Becht, E. et al. Dimensionality reduction for visualizing single-cell data using UMAP. Nat. Biotechnol. 37, 38–44 (2019).

    Article  CAS  Google Scholar 

  73. Hodge, R. D. et al. Conserved cell types with divergent features in human versus mouse cortex. Nature 573, 61–68 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  74. Zhang, Y. et al. Model-based analysis of ChIP-Seq (MACS). Genome Biol. 9, R137 (2008).

    Article  PubMed  PubMed Central  Google Scholar 

  75. Weirauch, M. T. et al. Determination and inference of eukaryotic transcription factor sequence specificity. Cell 158, 1431–1443 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  76. Robinson, J. T. et al. Integrative genomics viewer. Nat. Biotechnol. 29, 24–26 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  77. Falcon, S. & Gentleman, R. Using GOstats to test gene lists for GO term association. Bioinformatics 23, 257–258 (2007).

    Article  CAS  PubMed  Google Scholar 

  78. Chen, J., Bardes, E. E., Aronow, B. J. & Jegga, A. G. ToppGene suite for gene list enrichment analysis and candidate gene prioritization. Nucleic Acids Res. 37, W305–W311 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  79. Banerjee-Basu, S. & Packer, A. SFARI Gene: an evolving database for the autism research community. Dis. Models Mech. 3, 133–135 (2010).

    Article  Google Scholar 

  80. Velmeshev, D. et al. Single-cell genomics identifies cell type-specific molecular changes in autism. Science 364, 685–689 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  81. Mathys, H. et al. Single-cell transcriptomic analysis of Alzheimer’s disease. Nature 570, 332–337 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  82. de Leeuw, C. A., Mooij, J. M., Heskes, T. & Posthuma, D. MAGMA: generalized gene-set analysis of GWAS data. PLoS Comput. Biol. 11, e1004219 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  83. Grove, J. et al. Identification of common genetic risk variants for autism spectrum disorder. Nat. Genet. 51, 431–444 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  84. Jansen, I. E. et al. Genome-wide meta-analysis identifies new loci and functional pathways influencing Alzheimer’s disease risk. Nat. Genet. 51, 404–413 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  85. International League Against Epilepsy Consortium on Complex Epilepsies. Genome-wide mega-analysis identifies 16 loci and highlights diverse biological mechanisms in the common epilepsies. Nat. Commun. 9, 5269 (2018).

    Article  Google Scholar 

  86. Lee, J. J. et al. Gene discovery and polygenic prediction from a genome-wide association study of educational attainment in 1.1 million individuals. Nat. Genet. 50, 1112–1121 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  87. Savage, J. E. et al. Genome-wide association meta-analysis in 269,867 individuals identifies new genetic and functional links to intelligence. Nat. Genet. 50, 912–919 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  88. Bipolar Disorder and Schizophrenia Working Group of the Psychiatric Genomics Consortium.Genomic dissection of bipolar disorder and schizophrenia, including 28 subphenotypes. Cell 173, 1705–1715.e16 (2018).

    Article  PubMed Central  Google Scholar 

  89. Davies, G. et al. Study of 300,486 individuals identifies 148 independent genetic loci influencing general cognitive function. Nat. Commun. 9, 2098 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  90. Wray, N. R. et al. Genome-wide association analyses identify 44 risk variants and refine the genetic architecture of major depression. Nat. Genet. 50, 668–681 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  91. Pardinas, A. F. et al. Common schizophrenia alleles are enriched in mutation-intolerant genes and in regions under strong background selection. Nat. Genet. 50, 381–389 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  92. Martin, J. et al. A genetic investigation of sex bias in the prevalence of attention-deficit/hyperactivity disorder. Biol. Psychiatry 83, 1044–1053 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  93. Sohail, M. et al. Polygenic adaptation on height is overestimated due to uncorrected stratification in genome-wide association studies. eLife https://doi.org/10.7554/eLife.39702 (2019).

  94. Hoffmann, T. J. et al. A large multiethnic genome-wide association study of adult body mass index identifies novel loci. Genetics 210, 499–515 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  95. Morris, A. P. et al. Large-scale association analysis provides insights into the genetic architecture and pathophysiology of type 2 diabetes. Nat. Genet. 44, 981–990 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  96. Estrada, K. et al. Genome-wide meta-analysis identifies 56 bone mineral density loci and reveals 14 loci associated with risk of fracture. Nat. Genet. 44, 491–501 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  97. Schunkert, H. et al. Large-scale association analysis identifies 13 new susceptibility loci for coronary artery disease. Nat. Genet. 43, 333–338 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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Acknowledgements

We thank the patients for participating in the study and the donors and their families for the additional tissue samples. We also thank K. Gleason for assistance with postmortem samples. G.K. is supported by a Jon Heighten Scholarship in Autism Research at UT Southwestern. This work was supported by NIMH (F30MH105158) to M.R.F.; NIDA (5T32DA007290-25) and NHBLI (1T32HL139438-01A1) to F.A.; NINDS (NS106447), a UT BRAIN Initiative Seed Grant (366582), the Chilton Foundation, and the National Center for Advancing Translational Sciences of the NIH under the Center for Translational Medicine’s award number UL1TR001105 to B.C.L. and G.K.; NINDS (NS107357) to B.C.L.; and NIMH (MH103517), The Chan Zuckerberg Initiative, an advised fund of Silicon Valley Community Foundation (HCA-A-1704-01747), and the James S. McDonnell Foundation 21st Century Science Initiative in Understanding Human Cognition—Scholar Award (220020467) to G.K. Postmortem human tissue samples were obtained from the NIH NeuroBioBank (The Harvard Brain Tissue Resource Center, funded through HHSN-271-2013-537 00030C; the Human Brain and Spinal Fluid Resource Center, VA West Los Angeles Healthcare Center; and the University of Miami Brain Endowment Bank) and the UT Neuropsychiatry Research Program (Dallas Brain Collection). We also thank the UT Southwestern Neuroscience Microscopy Facility for providing imaging resources.

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Authors and Affiliations

  1. Department of Neuroscience, UT Southwestern Medical Center, Dallas, TX, USA

    Stefano Berto, Miles R. Fontenot, Fatma Ayhan, Emre Caglayan, Ashwinikumar Kulkarni, Connor Douglas & Genevieve Konopka

  2. Department of Neurosurgery, UT Southwestern Medical Center, Dallas, TX, USA

    Sarah Seger & Bradley C. Lega

  3. Department of Psychiatry, UT Southwestern Medical Center, Dallas, TX, USA

    Carol A. Tamminga

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  1. Stefano Berto
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Contributions

S.B., B.C.L. and G.K. analyzed the data and wrote the paper. M.R.F. and C.D. collected surgical samples, processed RNA and generated bulk RNA-seq libraries. M.R.F. contributed to the design of the project. F.A. generated the snRNA-seq and snATAC-seq data and performed IHC. A.K. preprocessed the snRNA-seq data. E.C. preprocessed the snATAC-seq data. C.A.T. provided postmortem human brain tissue. S.S. analyzed the oscillation data. B.C.L. conducted all surgical procedures and memory testing. B.C.L. and G.K. designed and supervised the study and provided intellectual guidance. All authors discussed the results and commented on the manuscript.

Corresponding authors

Correspondence to Bradley C. Lega or Genevieve Konopka.

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The authors declare no competing interests.

Additional information

Peer review information Nature Neuroscience thanks Andrew Jaffe, Ueli Rutishauser, Ziv Williams, and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Extended data

Extended Data Fig. 1 Data quality control.

a, Box plots depicting the probability of recall for items presented at each serial position. Primacy and recency effects are visible, consistent with expectations for performance in the free recall episodic memory paradigm. Whiskers on box plots represent maximum and minimum values. Boxes extend from the 25th to the 75th percentiles, the center lines represent the median. Loess regression with confidence intervals is superimposed to depict the overall distribution. Smooth curves are shown with 95% confidence bands b, Lag conditional response probability curves in our data (lag CRP), indicating expected temporal clustering behavior. Loess regression with confidence intervals depicts the overall distribution. Smooth curves are shown with 95% confidence bands. c, Boxplot showing the comparison of within-subject variance (across all measured electrodes at each band, blue box plot,) with the variance across subjects (at each band, yellow box plot). Across subjects variance is significantly greater than within-subject variance. Reported p-value from Wilcoxon rank sum test (one-sided with alternative greater). Boxplots extend from the 25th to the 75th percentiles, the center lines represent the median. d, Scatter plot showing the fraction of all BA38 electrodes exhibiting a significant subsequent memory effect at each frequency. We observed significant differences predicting recall success across the frequency spectrum, including the delta and gamma bands. Loess regression with confidence intervals depicts the overall distribution. Smooth curves are shown with 95% confidence bands. e, Distribution of SME values for each brain oscillation and cross-correlation based on Spearman’s rank correlation. f, Barplots showing the fraction of electrodes at which oscillations were detected in each frequency band in the recalled and non-recalled conditions. 85% of electrodes exhibited an oscillation in at least one of the delta, theta, or alpha frequency bands. g, Scatter plot showing individual electrode examples of power curves used for oscillation detection via the MODAL algorithm, both before and after subtraction of the best fit line. h, Principal component analysis of the subjects used for the within-subject analysis. Variance explained by each principal component is highlighted in the axis. i, Barplot showing the variance explained by each covariate adjusted across 10 principal components (wVE) for the within-subject data. Technical, biological and sequencing covariates calculated by PICARD (see Methods) are included. l, Principal component analysis of all the subjects used in this study. PMep = post-mortem epileptic subjects, UT = within-subjects, PMctl = post-mortem healthy subjects. m, Variance explained by each covariate adjusted across 10 principal components (wVE). Type corresponds to the three different types of data included in the analysis (PMep, UT, PMctl). Technical, biological and sequencing covariates calculated by PICARD (see Methods) are included. n, Association between the first two components and covariates based on adjusted gene expression. X-axis corresponds to the -log10(P-value) from linear regression modeling between PCs and covariates.

Extended Data Fig. 2 SME gene robustness and overlap with other tasks.

a, Boxplot showing the difference between F-statistics of the SME genes (Multivariate analysis) compared with the other genes. Stars correspond to the Wilcoxon’s rank sum test (N, Sign = 753, NotSign = 14439; one-sided with alternative greater; p < 0.0001 = ****; Benjamini-Hochberg adjusted: Delta, FDR = 2.3x10−249, Theta, FDR = 3.2x10-205, Alpha, FDR = 4.1x10-140, Beta, FDR = 2.1x10-159, Low Gamma, FDR = 7.2x10-207, High Gamma, FDR = 1.3x10-63). Boxes extend from the 25th to the 75th percentiles and the center lines represent the median. b, Violin plots showing the rho^2 of the genes significantly associated with each brain oscillation. Standard errors are calculated based on the rho^2 distribution of the significantly correlated genes. Dots represent the median rho^2 for the specific brain oscillation. c, Violin plots showing the rho^2 of the genes significantly associated with each brain oscillation (Obs = observed) compared with rho^2 derived from the permutation control analyses (Perm = Permutation). Standard errors are calculated based on the rho^2 distribution of the significantly correlated genes. Dots represent the median rho^2 for the specific brain oscillation. 100 random permutations were applied to calculate the Perm values (see Methods). Stars correspond to the Wilcoxon’s rank sum test (unadjusted, one-sided with alternative greater; p < 0.0001 = ****).

Extended Data Fig. 3 WGCNA highlights modules associated with memory oscillations.

a, Representative network dendrogram for the consensus WGCNA. Heatmap shows the correlation between memory oscillatory signatures and genes. Red = positively correlated, Blue = negatively correlated. b, Heatmap showing the module association between memory oscillatory signatures and module eigengenes (Spearman’s rank correlation). Warm colors represent positive correlations and cool colors represent negative correlations. P-values for each correlation together with exact correlation values are contained within each box. c, Bubble-chart showing the enrichment for 300 SME genes decomposed by brain oscillation. Gradient color represents the -log10(FDR) and bubble size represents the odds ratio (OR) from a Fisher’s exact enrichment test of each module with disease-relevant gene lists. Y-axis shows the brain oscillations labels. X-axis indicates the modules of the present study. d, Boxplots showing the differential connectivity (for example number of edges) between SME genes and non-SME genes in the modules associated with memory oscillatory signatures with SME genes enriched. Stars correspond to the results of a Wilcoxon’s rank sum test (one-side test with alternative greater; p < 0.001 = ****, p < 0.01 = **, p < 0.05 = *; Benjamini-Hochberg adjusted: WM4, FDR = 0.016, WM12, FDR = 0.048, WM21, FDR = 4.5x10-4). Boxes extend from the 25th to the 75th percentiles and the center lines represent the median.

Extended Data Fig. 4 Memory-related modules are enriched for gene co-expression modules associated with neuropsychiatric disorders.

a, Bubble-chart showing the enrichment for loci associated with human traits used as negative controls. Gradient color represents the -log10(FDR) from linkage disequilibrium gene set analysis performed by MAGMA. Y-axis shows the acronyms for the GWAS data utilized for this analysis (see Methods). b, Bubble-chart showing the enrichment for modules of co-expressed genes dysregulated in ASD, SCZ or BD. Gradient color represents the -log10(FDR) and bubble size represents the odds ratio (OR) from a Fisher’s exact enrichment test. Y-axis shows the acronyms for the modules associated with neuropsychiatric disorders utilized for this analysis (see Methods). X-axis shows the modules of the present study. Modules significantly correlated with memory-related oscillations are highlighted in bold text.

Extended Data Fig. 5 snRNA-seq quality control metrices and module enrichment for cell-types dysregulated in cognitive disorders.

a, Barplot showing the total number of nuclei identified per subject. Colors correspond to the two different batches. b, Quality control boxplots for snRNA-seq with number of genes detected, number of UMIs and percentage of mitochondrial genes. Colors correspond to the two different batches. Boxes extend from the 25th to the 75th percentiles and the center lines represent the median. Dots represent outliers. c, Scatter plot showing the relationship between number of UMIs (X-axis) and detected genes (Y-axis). Each sample is indicated in a different color. d, UMAP plots showing the distribution of nuclei in each subject. Colors correspond to the two different batches. e, Proportion of nuclei representing the identified clusters. Colors correspond to the six different subjects analyzed. f, UMAP plots showing the distribution of the three major cell-classes: GABAergic (blue), Glutamatergic (red), and non-neuronal (green). g, Pie chart showing the proportion of the three major cell-type classes (GABAergic, Glutamatergic, and non-neuronal cells). h, Bubble-chart showing the enrichment of the SME modules for cell-type markers dysregulated in ASD. Color gradient represents the -log10(FDR) and bubble size represent the odds ratio (OR) from a Fisher’s exact enrichment test. Y-axis shows the acronyms for the cell-types defined in the ASD study. i, Bubble-chart showing the enrichment of the SME modules for cell-type markers dysregulated in Alzheimer disease (AD). Color gradient represents the -log10(FDR) and bubble size represent the odds ratio (OR) from a Fisher’s exact enrichment test. Y-axis shows the acronyms for the cell-types defined in the AD study.

Extended Data Fig. 6 snATAC-seq quality control metrices.

a, Barplot showing the total number of nuclei identified per subject. b, Quality control boxplots for each snATAC-seq sample demonstrating the total number of peaks, the number of reads in the peaks and the percentage of reads in peaks. Boxes extend from the 25th to the 75th percentiles and the center lines represent the median. Dots represent outliers. c, Scatter plot showing the relationship between total number of reads (X-axis) and percentage of reads in the peaks (Y-axis). Each sample is indicated in a different color. d, Heatmap of the pairwise similarity between cluster identities. Y-axis shows the snRNA-seq clusters. X-axis shows the snATAC-seq clusters. Gradient corresponds to the percentage of cells for the corresponding prediction label. e, UMAP plots showing the distribution of nuclei in each subject. f, UMAP plots showing the distribution of the three major cell-classes: GABAergic (blue), Glutamatergic (red), and non-neuronal (green). g, Pie chart showing the proportion of the three major cell-type classes.

Supplementary information

Reporting Summary

Supplementary Table 1

Demographic data. Demographic data with participants, technical covariates, biological covariates, sequencing covariates, SME data, math data, MRI data and behavioral performance metrics.

Supplementary Table 2

Memory biomarkers: gene expression statistics and gene association. All statistics for genes correlated with each brain oscillation and database for association with previous studies.

Supplementary Table 3

Consensus WGCNA modules and gene set association. All statistics for module detection, overlap with memory-correlated genes and database for association with previous studies.

Supplementary Table 4

MAGMA summary statistics for consensus WGCNA modules. All statistics for GWAS enrichment in memory-associated modules.

Supplementary Table 5

snRNA-seq cell-type markers. Genes differentially expressed in each cell type with relative statistics.

Supplementary Table 6

snATAC-seq motif enrichment for peaks associated with module in specific cell types. Motif enrichment statistics for memory modules associated with specific cell types.

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Berto, S., Fontenot, M.R., Seger, S. et al. Gene-expression correlates of the oscillatory signatures supporting human episodic memory encoding. Nat Neurosci 24, 554–564 (2021). https://doi.org/10.1038/s41593-021-00803-x

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