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Rupprecht, R.

Publications and source records attributed to Rupprecht, R..

2 recordsLinked to original sources

Supracategorical fear information revealed by aversively conditioning multiple categories

Fear-generalization is a critical function for survival, in which an organism extracts information from a specific instantiation of a threat (e.g., the western diamondback rattlesnake in my front yard on Sunday) and learns to fear--and accordingly respond to--pertinent higher-order information (e.g., snakes live in my yard). Previous work investigating fear-conditioning in humans has used functional magnetic resonance imaging (fMRI) to demonstrate that activity-patterns of stimuli from an aversively-conditioned category (CS+) are more similar to each other than those of a neutral category (CS-). Here we designed a three-phase (i.e., baseline, conditioned, extinction) experiment using fMRI and multiple aversively-conditioned categories to ask whether we would find only similarity increases within the CS+ categories or also an increase in similarity between the CS+ categories. Using representational similarity analysis, we correlated a set of models to activity-patterns underlying several regions of interest and found that, following fear-conditioning, between-category and within-category similarity increased for the CS+ categories in the superior frontal gyrus (SFG) and the right temporal pole (rTP). Activity patterns in the object-selective lateral occipital cortex tended to prefer the semantic model, regardless of the experimental phase. These results advance prior pattern-based neuroimaging work by exploring the effect of aversively-conditioning multiple categories and indicate an extended role for the SFG and rTP in potentially linking discrete information or abstractly representing supracategorical information during fear-learning for the purpose of proper generalization.

neuroscience

ConvDip: A convolutional neural network for better M/EEG Source Imaging

1The EEG is a well-established non-invasive method in neuroscientific research and clinical diagnostics. It provides a high temporal but low spatial resolution of brain activity. In order to gain insight about the spatial dynamics of the EEG one has to solve the inverse problem, i.e. finding the neural sources that give rise to the recorded EEG activity. The inverse problem is ill-posed, which means that more than one configuration of neural sources can evoke one and the same distribution of EEG activity on the scalp. Artificial neural networks have been previously used successfully to find either one or two dipoles sources. These approaches, however, have never solved the inverse problem in a distributed dipole model with more than two dipole sources. We present ConvDip, a novel convolutional neural network (CNN) architecture that solves the EEG inverse problem in a distributed dipole model based on simulated EEG data. We show that (1) ConvDip learned to produce inverse solutions from a single time point of EEG data and (2) outperforms state-of-the-art methods on all focused performance measures. It is more flexible when dealing with varying number of sources, produces less ghost sources and misses less real sources than the comparison methods. It produces plausible inverse solutions for real EEG recordings from human participants. (4) The trained network needs less than 40 ms for a single prediction. Our results qualify ConvDip as an efficient and easy-to-apply novel method for source localization in EEG data, with high relevance for clinical applications, e.g. in epileptology and real time applications.

neuroscience