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Effective reduced diffusion-models: a data driven approach to the analysis of neuronal dynamics.

ABSTRACT: We introduce in this paper a new method for reducing neurodynamical data to an effective diffusion equation, either experimentally or using simulations of biophysically detailed models. The dimensionality of the data is first reduced to the first principal component, and then fitted by the stationary solution of a mean-field-like one-dimensional Langevin equation, which describes the motion of a Brownian particle in a potential. The advantage of such description is that the stationary probability density of the dynamical variable can be easily derived. We applied this method to the analysis of cortical network dynamics during up and down states in an anesthetized animal. During deep anesthesia, intracellularly recorded up and down states transitions occurred with high regularity and could not be adequately described by a one-dimensional diffusion equation. Under lighter anesthesia, however, the distributions of the times spent in the up and down states were better fitted by such a model, suggesting a role for noise in determining the time spent in a particular state.

SUBMITTER: Deco G

PROVIDER: S-EPMC2778141 | biostudies-literature | 2009 Dec

REPOSITORIES: biostudies-literature

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Effective reduced diffusion-models: a data driven approach to the analysis of neuronal dynamics.

Deco Gustavo G Martí Daniel D Ledberg Anders A Reig Ramon R Sanchez Vives Maria V MV

PLoS computational biology 20091204 12

We introduce in this paper a new method for reducing neurodynamical data to an effective diffusion equation, either experimentally or using simulations of biophysically detailed models. The dimensionality of the data is first reduced to the first principal component, and then fitted by the stationary solution of a mean-field-like one-dimensional Langevin equation, which describes the motion of a Brownian particle in a potential. The advantage of such description is that the stationary probabilit ...[more]

PMID: 19997490

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Project description:BackgroundMovement disorders can be associated with anti-neuronal antibodies.MethodsWe conducted a systematic review of cases with documented anti-neuronal antibodies in serum and/or cerebrospinal fluid published in PubMed before April 1, 2020. Only patients with at least one movement disorder were included. We used random forests for variable selection and recursive partitioning and regression trees for the creation of a data-driven decision algorithm, integrated with expert's clinical feedback.ResultsThree hundred and seventy-seven studies met eligibility criteria, totaling 844 patients and 13 antibodies: amphiphysin, GAD, GlyR, mGluR1, ANNA-2/Ri, Yo/PCA-1, Caspr2, NMDAR, LGI-1, CRMP5/CV2, ANNA-1/Hu, IgLON5, and DPPX. Stiffness/rigidity/spasm spectrum symptoms were more frequently associated with amphiphysin, GAD, and GlyR; ataxia with mGluR1, ANNA-2/Ri, Yo/PCA-1, Caspr2, and ANNA-1/Hu; dyskinesia with NMDAR and paroxysmal movement with LGI1; chorea/choreoathetosis with CRMP5/CV2, IgLON5, and NMDAR; myoclonus with GlyR and DPPX; tremors with ANNA2/Ri and anti-DPPX; and parkinsonism with IgLON5 and NMDAR. Data-driven classification analysis determined the following diagnostic predictions (with probability selection): psychiatric symptoms and dyskinesia predicted NMDAR (71% and 87%, respectively); stiffness/rigidity/spasm and ataxia, GAD (67% and 47%, respectively); ataxia and opsoclonus, ANNA-2/Ri (68%); chorea/choreoathetosis, CRMP5/CV2 (41%). These symptoms remained the top predictors in random forests analysis. The integration with an expert opinion analysis refined the precision of the approach. Breast and lung tumors were the most common tumors. On neuroimaging, cerebellar involvement was associated with GAD and Yo/PCA-1; temporal involvement with Caspr2, LGI-1, ANNA-1/Hu.ConclusionSelected movement disorders are associated with specific anti-neuronal antibodies. The combination of data-driven and expert opinion approach to the diagnosis may assist early management efforts.

Dataset Information

Effective reduced diffusion-models: a data driven approach to the analysis of neuronal dynamics.

Publications

Effective reduced diffusion-models: a data driven approach to the analysis of neuronal dynamics.

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