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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The RNA-seq dataset used for memory oscillatory signature analysis in this study are available at GEO with accession number GSE139914.
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.
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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.
The authors declare no competing interests.
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.
Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
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.
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 = ****).
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.
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.
Demographic data. Demographic data with participants, technical covariates, biological covariates, sequencing covariates, SME data, math data, MRI data and behavioral performance metrics.
Memory biomarkers: gene expression statistics and gene association. All statistics for genes correlated with each brain oscillation and database for association with previous studies.
Consensus WGCNA modules and gene set association. All statistics for module detection, overlap with memory-correlated genes and database for association with previous studies.
MAGMA summary statistics for consensus WGCNA modules. All statistics for GWAS enrichment in memory-associated modules.
snRNA-seq cell-type markers. Genes differentially expressed in each cell type with relative statistics.
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
Nature Neuroscience (2021)