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Neonatal cortical surface atlas using Multimodal Surface Matching in the Developing Human Connectome Project (2018)

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Code and data repository accompanying: Gutzen, R., Grün, S., Denker, M., 2023. Evaluating the statistical similarity of neural network activity and connectivity via eigenvector angles. Biosystems 223, 104813. https://doi.org/10.1016/j.biosystems.2022.104813

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Hypoxic-Ischemic Encephalopathy

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ABI mouse brain connectivity data in BIDS/NIfTI form, alongside generator code.

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TemplateFlow-compliant version of the volumetric dHCP infant templates.

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TemplateFlow-compliant version of the volumetric dHCP infant templates.

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Uruguayan Chatbot Project - Code

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dhcpSym spatiotemporal cortical surface atlas

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Longitudinal (8 wks) T2-weighted MRI and resting-state functional MRI (rs-fMRI) of WT mice with cortical stroke

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Data and code for: Directed information exchange between cortical layers in V1 and V4 and its modulation by selective attention. Data contain simultaneous ephys recording of LFPs in V1 and V4 during covert, visual attention behavior. Code runs characterization of spectral power, coherence, and custom designed Conditional Granger Causality (cGC) based on Mutual Information extraction for layer-wise and contact-wise interplay between and within cortical areas.

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In this online study, 202 participants completed fear acquisition (ACQ) on day 1 and fear generalisation (GEN) on day 2. Conditioned stimuli (CS) were two black rings; for GEN, 8 stimuli (GS2-GS9) with 15% size increments between CS+ and CS- were used. Unconditioned stimuli consisted of unpleasant and neutral pictures. 4 groups were tested: non-smokers, smokers who stopped smoking 6h post-ACQ, smokers who smoked post-ACQ, and smokers who smoked pre-ACQ. Additional analyses compared smokers and non-smokers.

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The tool allows to retrieve and fit empirical data to the distribution of folded Gaussian random variables, including the distribution of sum and difference of pairs of random variables.

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The function computes Latency indices base don MUAes fitted by cumulative gaussian function with 5 (degrees of freedom) parameters (mu, sigma, alpha, c, d). The best fit is assigned based on Least Squares (LS) of iterative estimation.

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The code computes Currsent Source Densities via the iCSD Cubic Splines method published in (Pettersen et al., J Neurosci Methods, 2006) developed for (Ferro, van Kempen, Boyd, Panzeri, Thiele, PNAS 2021).

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OpenNeuro Average (onavg) surface template

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