Single-Cell Transcriptomic Evidence for Dense Intracortical Neuropeptide Networks, bioRxiv, 2019-01-14

BrieflyAnalysis of single-cell RNA-Seq data from mouse neocortex exposes evidence for local neuropeptidergic modulation networks that involve every cortical neuron directly.Data Highlights<jatslist list-type=bullet><jatslist-item>At least 98% of mouse neocortical neurons express one or more of 18 neuropeptide precursor proteins (NPP) genes.<jatslist-item><jatslist-item>At least 98% of cortical neurons express one or more of 29 neuropeptide-selective G-protein-coupled receptor (NP-GPCR) genes.<jatslist-item><jatslist-item>Neocortical expression of these 18 NPP and 29 NP-GPCR genes is highly neuron-type-specific and permits exceptionally powerful differentiation of transcriptomic neuron types.<jatslist-item><jatslist-item>Neuron-type-specific expression of 37 cognate NPP NP-GPCR gene pairs predicts modulatory connectivity within 37 or more neuron-type-specific intracortical networks.<jatslist-item>SummarySeeking insight into homeostasis, modulation and plasticity of cortical synaptic networks, we analyzed results from deep RNA-Seq analysis of 22,439 individual mouse neocortical neurons. This work exposes transcriptomic evidence that all cortical neurons participate directly in highly multiplexed networks of modulatory neuropeptide (NP) signaling. The evidence begins with a discovery that transcripts of one or more neuropeptide precursor (NPP) and one or more neuropeptide-selective G-protein-coupled receptor (NP-GPCR) genes are highly abundant in nearly all cortical neurons. Individual neurons express diverse subsets of NP signaling genes drawn from a palette encoding 18 NPPs and 29 NP-GPCRs. Remarkably, these 47 genes comprise 37 cognate NPPNP-GPCR pairs, implying a strong likelihood of dense, cortically localized neuropeptide signaling. Here we use neuron-type-specific NP gene expression signatures to put forth specific, testable predictions regarding 37 peptidergic neuromodulatory networks that may play prominent roles in cortical homeostasis and plasticity.

biorxiv neuroscience 100-200-users 2019

Individual-Specific fMRI-Subspaces Improve Functional Connectivity Prediction of Behavior Supplemental, bioRxiv, 2019-01-10

There is significant interest in using resting-state functional connectivity (RSFC) to predict human behavior. Good behavioral prediction should in theory require RSFC to be sufficiently distinct across participants; if RSFC were the same across participants, then behavioral prediction would obviously be poor. Therefore, we hypothesize that removing common resting-state functional magnetic resonance imaging (rs-fMRI) signals that are shared across participants would improve behavioral prediction. Here, we considered 803 participants from the human connectome project (HCP) with four rs-fMRI runs. We applied the common and orthogonal basis extraction (COBE) technique to decompose each HCP run into two subspaces a common (group-level) subspace shared across all participants and a subject-specific subspace. We found that the first common COBE component of the first HCP run was localized to the visual cortex and was unique to the run. On the other hand, the second common COBE component of the first HCP run and the first common COBE component of the remaining HCP runs were highly similar and localized to regions within the default network, including the posterior cingulate cortex and precuneus. Overall, this suggests the presence of run-specific (state-specific) effects that were shared across participants. By removing the first and second common COBE components from the first HCP run, and the first common COBE component from the remaining HCP runs, the resulting RSFC improves behavioral prediction by an average of 11.7% across 58 behavioral measures spanning cognition, emotion and personality.

biorxiv neuroscience 0-100-users 2019

 

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