Dataset Information

Imaging cortical dynamics of language processing with the event-related optical signal.

ABSTRACT: Language processing involves the rapid interaction of multiple brain regions. The study of its neurophysiological bases would therefore benefit from neuroimaging techniques combining both good spatial and good temporal resolution. Here we use the event-related optical signal (EROS), a recently developed imaging method, to reveal rapid interactions between left superior/middle temporal cortices (S/MTC) and inferior frontal cortices (IFC) during the processing of semantically or syntactically anomalous sentences. Participants were presented with sentences of these types intermixed with nonanomalous control sentences and were required to judge their acceptability. ERPs were recorded simultaneously with EROS and showed the typical activities that are elicited when processing anomalous stimuli: the N400 and the P600 for semantic and syntactic anomalies, respectively. The EROS response to semantically anomalous words showed increased activity in the S/MTC (corresponding in time with the N400), followed by IFC activity. Syntactically anomalous words evoked a similar sequence, with a temporal-lobe EROS response (corresponding in time with the P600), followed by frontal activity. However, the S/MTC activity corresponding to a semantic anomaly was more ventral than that corresponding to a syntactic anomaly. These data suggest that activation related to anomaly processing in sentences proceeds from temporal to frontal brain regions for both semantic and syntactic anomalies. This first EROS study investigating language processing shows that EROS can be used to image rapid interactions across cortical areas.

SUBMITTER: Tse CY

PROVIDER: S-EPMC2040398 | biostudies-literature | 2007 Oct

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Imaging cortical dynamics of language processing with the event-related optical signal.

Tse Chun-Yu CY Lee Chia-Lin CL Sullivan Jason J Garnsey Susan M SM Dell Gary S GS Fabiani Monica M Gratton Gabriele G

Proceedings of the National Academy of Sciences of the United States of America 20071017 43

Language processing involves the rapid interaction of multiple brain regions. The study of its neurophysiological bases would therefore benefit from neuroimaging techniques combining both good spatial and good temporal resolution. Here we use the event-related optical signal (EROS), a recently developed imaging method, to reveal rapid interactions between left superior/middle temporal cortices (S/MTC) and inferior frontal cortices (IFC) during the processing of semantically or syntactically anom ...[more]

PMID: 17942677

Similar Datasets

Project description:Second language (L2) learners experience challenges when word meanings differ across L1 and L2, and often display crosslinguistic influence (CLI) in speech production. In contrast, studies of online comprehension show more mixed results. Therefore, this study explored how L2 learners process fine-grained L2 verb semantics in the domain of caused motion (placement) and specifically the impact of having similar vs. non-similar semantics in the L1 and L2. Specifically, we examined English (20) and German (21) L2 learners of Swedish and native Swedish speakers (16) and their online neurophysiological processing and offline appropriateness ratings of three Swedish placement verbs obligatory for placement supported from below: sätta "set," ställa "stand," and lägga "lay." The learners' L1s differed from Swedish in that their placement verbs either shared or did not share semantic characteristics with the target language. English has a general placement verb put, whereas German has specific verbs similar but not identical to Swedish, stellen "set/stand" and legen "lay." Event-related potentials (ERPs) were recorded while participants watched still frames (images) of objects being placed on a table and listened to sentences describing the event with verbs that either matched the image or not. Participants also performed an offline appropriateness rating task. Both tasks suggested CLI. English learners' appropriateness ratings of atypical verb use differed from those of both native Swedish speakers' and German learners, with no difference in the latter pair. Similarly, German learners' ERP effects were more similar to those of the native Swedish speakers (increased lateral negativity to atypical verb use) than to those of the English learners (increased positivity to atypical verb use). The results of this explorative study thus suggest CLI both offline and online with similarity between L1 and L2 indicating more similar processing and judgments, in line with previous production findings, but in contrast to previous ERP work on semantic L2 processing.

Project description:According to the World Health Organization (WHO), around 60% of all outbreaks are detected using informal sources. In many public health institutes, including the WHO and the Robert Koch Institute (RKI), dedicated groups of public health agents sift through numerous articles and newsletters to detect relevant events. This media screening is one important part of event-based surveillance (EBS). Reading the articles, discussing their relevance, and putting key information into a database is a time-consuming process. To support EBS, but also to gain insights into what makes an article and the event it describes relevant, we developed a natural language processing framework for automated information extraction and relevance scoring. First, we scraped relevant sources for EBS as done at the RKI (WHO Disease Outbreak News and ProMED) and automatically extracted the articles' key data: disease, country, date, and confirmed-case count. For this, we performed named entity recognition in two steps: EpiTator, an open-source epidemiological annotation tool, suggested many different possibilities for each. We extracted the key country and disease using a heuristic with good results. We trained a naive Bayes classifier to find the key date and confirmed-case count, using the RKI's EBS database as labels which performed modestly. Then, for relevance scoring, we defined two classes to which any article might belong: The article is relevant if it is in the EBS database and irrelevant otherwise. We compared the performance of different classifiers, using bag-of-words, document and word embeddings. The best classifier, a logistic regression, achieved a sensitivity of 0.82 and an index balanced accuracy of 0.61. Finally, we integrated these functionalities into a web application called EventEpi where relevant sources are automatically analyzed and put into a database. The user can also provide any URL or text, that will be analyzed in the same way and added to the database. Each of these steps could be improved, in particular with larger labeled datasets and fine-tuning of the learning algorithms. The overall framework, however, works already well and can be used in production, promising improvements in EBS. The source code and data are publicly available under open licenses.

Project description:Language tasks require the coordinated activation of multiple subnetworks-groups of related cortical interactions involved in specific components of task processing. Although electrocorticography (ECoG) has sufficient temporal and spatial resolution to capture the dynamics of event-related interactions between cortical sites, it is difficult to decompose these complex spatiotemporal patterns into functionally discrete subnetworks without explicit knowledge of each subnetwork's timing. We hypothesized that subnetworks corresponding to distinct components of task-related processing could be identified as groups of interactions with co-varying strengths. In this study, five subjects implanted with ECoG grids over language areas performed word repetition and picture naming. We estimated the interaction strength between each pair of electrodes during each task using a time-varying dynamic Bayesian network (tvDBN) model constructed from the power of high gamma (70-110Hz) activity, a surrogate for population firing rates. We then reduced the dimensionality of this model using principal component analysis (PCA) to identify groups of interactions with co-varying strengths, which we term functional network components (FNCs). This data-driven technique estimates both the weight of each interaction's contribution to a particular subnetwork, and the temporal profile of each subnetwork's activation during the task. We found FNCs with temporal and anatomical features consistent with articulatory preparation in both tasks, and with auditory and visual processing in the word repetition and picture naming tasks, respectively. These FNCs were highly consistent between subjects with similar electrode placement, and were robust enough to be characterized in single trials. Furthermore, the interaction patterns uncovered by FNC analysis correlated well with recent literature suggesting important functional-anatomical distinctions between processing external and self-produced speech. Our results demonstrate that subnetwork decomposition of event-related cortical interactions is a powerful paradigm for interpreting the rich dynamics of large-scale, distributed cortical networks during human cognitive tasks.

Dataset Information

Imaging cortical dynamics of language processing with the event-related optical signal.

Publications

Imaging cortical dynamics of language processing with the event-related optical signal.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets