Dataset Information

Leave-One-Trial-Out, LOTO, a general approach to link single-trial parameters of cognitive models to neural data.

ABSTRACT: A key goal of model-based cognitive neuroscience is to estimate the trial-by-trial fluctuations of cognitive model parameters in order to link these fluctuations to brain signals. However, previously developed methods are limited by being difficult to implement, time-consuming, or model-specific. Here, we propose an easy, efficient and general approach to estimating trial-wise changes in parameters: Leave-One-Trial-Out (LOTO). The rationale behind LOTO is that the difference between parameter estimates for the complete dataset and for the dataset with one omitted trial reflects the parameter value in the omitted trial. We show that LOTO is superior to estimating parameter values from single trials and compare it to previously proposed approaches. Furthermore, the method makes it possible to distinguish true variability in a parameter from noise and from other sources of variability. In our view, the practicability and generality of LOTO will advance research on tracking fluctuations in latent cognitive variables and linking them to neural data.

SUBMITTER: Gluth S

PROVIDER: S-EPMC6392499 | biostudies-literature | 2019 Feb

REPOSITORIES: biostudies-literature

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Leave-One-Trial-Out, LOTO, a general approach to link single-trial parameters of cognitive models to neural data.

Gluth Sebastian S Meiran Nachshon N

eLife 20190208

A key goal of model-based cognitive neuroscience is to estimate the trial-by-trial fluctuations of cognitive model parameters in order to link these fluctuations to brain signals. However, previously developed methods are limited by being difficult to implement, time-consuming, or model-specific. Here, we propose an easy, efficient and general approach to estimating trial-wise changes in parameters: Leave-One-Trial-Out (LOTO). The rationale behind LOTO is that the difference between parameter es ...[more]

PMID: 30735125

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Project description:Leave-one-out green fluorescent protein (LOOn-GFP) is a circularly permuted and truncated GFP lacking the nth ?-strand element. LOO7-GFP derived from the wild-type sequence (LOO7-WT) folds and reconstitutes fluorescence upon addition of ?-strand 7 (S7) as an exogenous peptide. Computational protein design may be used to modify the sequence of LOO7-GFP to fit a different peptide sequence, while retaining the reconstitution activity. Here we present a computationally designed leave-one-out GFP in which wild-type strand 7 has been replaced by a 12-residue peptide (HA) from the H5 antigenic region of the Thailand strain of H5N1 influenza virus hemagglutinin. The DEEdesign software was used to generate a sequence library with mutations at 13 positions around the peptide, coding for approximately 3 × 10(5) sequence combinations. The library was coexpressed with the HA peptide in E. coli and colonies were screened for in vivo fluorescence. Glowing colonies were sequenced, and one (LOO7-HA4) with 7 mutations was purified and characterized. LOO7-HA4 folds, fluoresces in vivo and in vitro, and binds HA. However, binding results in a decrease in fluorescence instead of the expected increase, caused by the peptide-induced dissociation of a novel, glowing oligomeric complex instead of the reconstitution of the native structure. Efforts to improve binding and recover reconstitution using in vitro evolution produced colonies that glowed brighter and matured faster. Two of these were characterized. One lost all affinity for the HA peptide but glowed more brightly in the unbound oligomeric state. The other increased in affinity to the HA peptide but still did not reconstitute the fully folded state. Despite failing to fold completely, peptide binding by computational design was observed and was improved by directed evolution. The ratio of HA to S7 binding increased from 0.0 for the wild-type sequence (no binding) to 0.01 after computational design (weak binding) and to 0.48 (comparable binding) after in vitro evolution. The novel oligomeric state is composed of an open barrel.

Project description:Neural mass models (NMMs) are important for helping us interpret observations of brain dynamics. They provide a means to understand data in terms of mechanisms such as synaptic interactions between excitatory and inhibitory neuronal populations. To interpret data using NMMs we need to quantitatively compare the output of NMMs with data, and thereby find parameter values for which the model can produce the observed dynamics. Mapping dynamics to NMM parameter values in this way has the potential to improve our understanding of the brain in health and disease. Though abstract, NMMs still comprise of many parameters that are difficult to constrain a priori. This makes it challenging to explore the dynamics of NMMs and elucidate regions of parameter space in which their dynamics best approximate data. Existing approaches to overcome this challenge use a combination of linearising models, constraining the values they can take and exploring restricted subspaces by fixing the values of many parameters a priori. As such, we have little knowledge of the extent to which different regions of parameter space of NMMs can yield dynamics that approximate data, how nonlinearities in models can affect parameter mapping or how best to quantify similarities between model output and data. These issues need to be addressed in order to fully understand the potential and limitations of NMMs, and to aid the development of new models of brain dynamics in the future. To begin to overcome these issues, we present a global nonlinear approach to recovering parameters of NMMs from data. We use global optimisation to explore all parameters of nonlinear NMMs simultaneously, in a minimally constrained way. We do this using multi-objective optimisation (multi-objective evolutionary algorithm, MOEA) so that multiple data features can be quantified. In particular, we use the weighted horizontal visibility graph (wHVG), which is a flexible framework for quantifying different aspects of time series, by converting them into networks. We study EEG alpha activity recorded during the eyes closed resting state from 20 healthy individuals and demonstrate that the MOEA performs favourably compared to single objective approaches. The addition of the wHVG objective allows us to better constrain the model output, which leads to the recovered parameter values being restricted to smaller regions of parameter space, thus improving the practical identifiability of the model. We then use the MOEA to study differences in the alpha rhythm observed in EEG recorded from 20 people with epilepsy. We find that a small number of parameters can explain this difference and that, counterintuitively, the mean excitatory synaptic gain parameter is reduced in people with epilepsy compared to control. In addition, we propose that the MOEA could be used to mine for the presence of pathological rhythms, and demonstrate the application of this to epileptiform spike-wave discharges.

Dataset Information

Leave-One-Trial-Out, LOTO, a general approach to link single-trial parameters of cognitive models to neural data.

Publications

Leave-One-Trial-Out, LOTO, a general approach to link single-trial parameters of cognitive models to neural data.

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