Electrophysiologically-defined excitation-inhibition autism neurosubtypes

This repository has all the code and tidy data for the analyses in Bertelsen, N., Mancini, G., Sastre-Yagüe, D., Vitale, A., Lorenz, G. M., Malerba, S. B., Bolis, D., Mandelli, V., Martínez-Cañada, P., Gozzi, A., Panzeri, S., & Lombardo, M. V. Electrophysiologically-defined excitation-inhibition autism neurosubtypes. medRxiv. doi:10.1101/2023.11.22.23298729. (https://www.medrxiv.org/content/10.1101/2023.11.22.23298729v1)

The data utilized in this work comes from the publicly available CMI-HBN dataset (http://fcon_1000.projects.nitrc.org/indi/cmi_healthy_brain_network/). For more details about the CMI-HBN data see Alexander et al., 2017, Scientific Data (https://www.nature.com/articles/sdata2017181).

The code directory within this repository is organized to have all the main initial steps (within the code/pp directory) and will also have all the other code for downstream data analysis within it. Below is a description of each step of the analyses, with the code, data, and results to expect/use.

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General requirements, assumptions, dependencies

Analyses heavily depend on use of MATLAB, R, and Python. For example, reval is implemented in Python. MATLAB is heavily relied upon for EEG preprocessing, computation of the Hurst exponent (H), and PLS analysis. R is heavily relied upon for main downstream statistical analysis (e.g., linear mixed effect modeling, plotting data, SigClust analysis). Below is a list of primary libraries/toolboxes that primary components of the data analysis heavily rely upon.

• EEGLAB (https://eeglab.org) - Used ubiquitously throughout for processing and handling EEG data.

• reval (https://github.com/IIT-LAND/reval_clustering) - Used for stability-based relative clustering validation analyses.

• nonfractal (https://github.com/wonsang/nonfractal) - Used for computation of H.

• PLS toolbox in MATLAB (https://www.rotman-baycrest.on.ca/index.php?section=84) - Used for the PLS analysis.

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In-silico computational modeling

_insilico_analysis.Rmd or _insilico_analysis.html

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In-vivo chemogenetic analyses

_invivo_analysis.Rmd or _invivo_analysis.html

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Pipeline for the human analyses:

pp download, clean, preprocess, and postproc H

code:

• code for running these steps is within the code/pp directory

_00_master.sh specifies sequence of steps for running code to do all steps _0*.py scripts implement each step

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01 run reval to identify subtypes

code:

• s01_reval_adjH.ipynb

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02 check cluster consistency across rs eye conditions

code:

• s02_reval_adjH_subtypes_consistency.Rmd

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03 check subtypes with SigClust

code:

• s03_reval_adjH_sigClust_subtypes_validation.Rmd

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04 run linear model at the electrode level

code:

• s04_reval_adjH_electrode_subtypes_analysis.Rmd

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05 plotting topoplots: average H, mean abs H differences, F and p-val

code:

• s05_reval_adjH_electrode_subtypes_topoplots.mlx

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06 run PCA and reconstruct H data from PC4

code:

• s06_reval_adjH_runPCA.mlx

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07 run linear model at the PCA level

code:

• s07_reval_adjH_PCA_subtypes_analysis.Rmd

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08 plot effect size differences by subtype and across the five blocks for electrodes (all 93) and PCs (1, 2 and 3)

code:

• s08_reval_adjH_effectsizes_plots.Rmd

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09 calculate and plot effect sizes for rec H data (PC1+PC4)

code:

• s09_reval_adjH_effectsizes_recH.Rmd

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10 topoplots for effects sizes (H + recH data)

code:

• s10_reval_adjH_effectsizes_topoplots.mlx

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11 Pheno analyses

code:

• s11_reval_adjH_pheno_analysis.Rmd

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12 prepare dataframes for PLS

code:

• s12_reval_adjH_prepdata4PLS.Rmd

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13, 14 run PLS

code:

• s13_reval_adjH_runPLS.m
• s14_reval_adjH_compute_bootstrap_ci_pls.m

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16

code:

• s16_reval_adjH_plot_pls_results.Rmd

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17

code:

• s17_reval_adjH_pls_topoplots.mlx

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