NNMT: Mean-Field Based Analysis Tools for Neuronal Network Models

Layer, Moritz; Senk, Johanna; Essink, Simon; Meegen, Alexander van; Bos, Hannah; Helias, Moritz

doi:10.3389/fninf.2022.835657

Cited by 4 publications

(3 citation statements)

References 100 publications

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“…Our model can be used as a convenient template for computational studies of complex mechanisms such as the effects of neuromodulators on sensory signal processing. Being based on the LIF neuron model, mean-field analyses can provide mechanistic explanations of the population-level dynamics, and make predictions of network activities with different parameters or inputs without running simulations (Layer et al, 2022). This can help to facilitate the exploration of the effects of cell and connection properties of interneurons, and examine the hypotheses we have proposed on the main pathways underlying the suppression and enhancement of cell-type-specific activity (see Cell-Type-Specific Stimulation, Discussion).…”

Section: Discussionmentioning

confidence: 99%

A Layered Microcircuit Model of Somatosensory Cortex with Three Interneuron Types and Cell-Type-Specific Short-Term Plasticity

Jiang,

Qi,

Duarte

et al. 2023

Preprint

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Three major types of GABAergic interneurons, the parvalbumin- (PV), somatostatin- (SOM) and vasoactive intestinal peptide-expressing (VIP) cells, play critical but distinct roles in the cortical microcircuitry. Their inhibitory functions are shaped by their specific electrophysiology and connectivity. To study how this diversity contributes to the dynamics and signal processing in the cerebral cortex, we developed a multi-layer model incorporating biologically realistic parameters of these interneurons from rodent somatosensory cortex. With in vivo data as criteria, the model shows plausible resting-state activity and responses to sensory stimulation. With a protocol of cell-type-specific stimulation, network responses when activating different neuron types are examined. The model reproduces the experimentally observed inhibitory effects of PV and SOM cells and disinhibitory effect of VIP cells on excitatory cells. In addition, responses of VIP cells to cell-type-specific stimulation provide predictions for future experiments. We further create a version of the model incorporating cell-type-specific short-term synaptic plasticity (STP). While the ongoing activity with and without STP is similar, STP modulates the responses of SOM and VIP cells to cell-type-specific stimulation, presumably by changing the dominant inhibitory pathways. Our model can serve to explore the computational roles of inhibitory interneurons and short-term synaptic plasticity in sensory functions.

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Section: Discussionmentioning

confidence: 99%

A Layered Microcircuit Model of Somatosensory Cortex with Three Interneuron Types and Cell-Type-Specific Short-Term Plasticity

Jiang,

Qi,

Duarte

et al. 2023

Preprint

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“…Recent studies have indicated that the NNMT gene is associated with several important physiological and disease processes. For example, the NNMT gene may play a role in energy metabolism, fatty acid oxidation, liver fat accumulation, diabetes, obesity, and cardiovascular diseases [ 50 , 51 ]. Furthermore, multiple investigations have elucidated a notable correlation between NNMT gene expression and the initiation and advancement of tumors.…”

Section: Discussionmentioning

confidence: 99%

Discovery of a novel lipid metabolism-related gene signature to predict outcomes and the tumor immune microenvironment in gastric cancer by integrated analysis of single-cell and bulk RNA sequencing

Zhang,

Wang,

Tian

et al. 2023

Lipids Health Dis

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Gastric cancer (GC) is a pressing global clinical issue, with few treatment options and a poor prognosis. The onset and spread of stomach cancer are significantly influenced by changes in lipid metabolism-related pathways. This study aimed to discover a predictive signature for GC using lipid metabolism-related genes (LMRGs) and examine its correlation with the tumor immune microenvironment (TIME). Transcriptome data and clinical information from patients with GC were collected from the TCGA and GEO databases. Data from GC samples were analyzed using both bulk RNA-seq and single-cell sequencing of RNA (scRNA-seq). To identify survival-related differentially expressed LMRGs (DE-LMRGs), differential expression and prognosis studies were carried out. We built a predictive signature using LASSO regression and tested it on the TCGA and GSE84437 datasets. In addition, the correlation of the prognostic signature with the TIME was comprehensively analyzed. In this study, we identified 258 DE-LMRGs in GC and further screened seven survival-related DE-LMRGs. The results of scRNA-seq identified 688 differentially expressed genes (DEGs) between the three branches. Two critical genes (GPX3 and NNMT) were identified using the above two gene groups. In addition, a predictive risk score that relies on GPX3 and NNMT was developed. Survival studies in both the TCGA and GEO datasets revealed that patients categorized to be at low danger had a significantly greater prognosis than those identified to be at high danger. Additionally, by employing calibration plots based on TCGA data, the study demonstrated the substantial predictive capacity of a prognostic nomogram, which incorporated a risk score along with various clinical factors. Within the high-risk group, there was a noticeable abundance of active natural killer (NK) cells, quiescent monocytes, macrophages, mast cells, and activated CD4 + T cells. In summary, a two-gene signature and a predictive nomogram have been developed, offering accurate prognostic predictions for general survival in GC patients. These findings have the potential to assist healthcare professionals in making informed medical decisions and providing personalized treatment approaches.

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“…The workflow is structured using snakemake ( Köster and Rahmann, 2012 ). For the mean-field analysis, we used the NNMT toolbox ( Layer et al, 2022 ).…”

Section: Methodsmentioning

confidence: 99%

Multi-Scale Spiking Network Model of Human Cerebral Cortex

Pronold

Meegen

Vollenbroeker

et al. 2023

Preprint

Self Cite

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Although the structure of cortical networks provides the necessary substrate for their neuronal activity, the structure alone does not suffice to understand it. Leveraging the increasing availability of human data, we developed a multi-scale, spiking network model of human cortex to investigate the relationship between structure and dynamics. In this model, each area in one hemisphere of the Desikan-Killiany parcellation is represented by a 1mm2 column with a layered structure. The model aggregates data across multiple modalities, including electron microscopy, electrophysiology, morphological reconstructions, and DTI, into a coherent framework. It predicts activity on all scales from single-neuron spiking activity to the area-level functional connectivity. We compared the model activity against human electrophysiological data and human resting-state fMRI data. This comparison reveals that the model can reproduce both spiking statistics and fMRI correlations if the cortico-cortical connections are sufficiently strong. Furthermore, we show that a single-spike perturbation propagates through the network within a time close to the limit imposed by the delays.

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NNMT: Mean-Field Based Analysis Tools for Neuronal Network Models

Cited by 4 publications

References 100 publications

A Layered Microcircuit Model of Somatosensory Cortex with Three Interneuron Types and Cell-Type-Specific Short-Term Plasticity

A Layered Microcircuit Model of Somatosensory Cortex with Three Interneuron Types and Cell-Type-Specific Short-Term Plasticity

Discovery of a novel lipid metabolism-related gene signature to predict outcomes and the tumor immune microenvironment in gastric cancer by integrated analysis of single-cell and bulk RNA sequencing

Multi-Scale Spiking Network Model of Human Cerebral Cortex

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