Dataset Information

Brain Network Activation Analysis Utilizing Spatiotemporal Features for Event Related Potentials Classification.

ABSTRACT: The purpose of this study was to introduce an improved tool for automated classification of event-related potentials (ERPs) using spatiotemporally parcellated events incorporated into a functional brain network activation (BNA) analysis. The auditory oddball ERP paradigm was selected to demonstrate and evaluate the improved tool. Methods: The ERPs of each subject were decomposed into major dynamic spatiotemporal events. Then, a set of spatiotemporal events representing the group was generated by aligning and clustering the spatiotemporal events of all individual subjects. The temporal relationship between the common group events generated a network, which is the spatiotemporal reference BNA model. Scores were derived by comparing each subject's spatiotemporal events to the reference BNA model and were then entered into a support vector machine classifier to classify subjects into relevant subgroups. The reliability of the BNA scores (test-retest repeatability using intraclass correlation) and their utility as a classification tool were examined in the context of Target-Novel classification. Results: BNA intraclass correlation values of repeatability ranged between 0.51 and 0.82 for the known ERP components N100, P200, and P300. Classification accuracy was high when the trained data were validated on the same subjects for different visits (AUCs 0.93 and 0.95). The classification accuracy remained high for a test group recorded at a different clinical center with a different recording system (AUCs 0.81, 0.85 for 2 visits). Conclusion: The improved spatiotemporal BNA analysis demonstrates high classification accuracy. The BNA analysis method holds promise as a tool for diagnosis, follow-up and drug development associated with different neurological conditions.

SUBMITTER: Stern Y

PROVIDER: S-EPMC5167752 | biostudies-literature | 2016

REPOSITORIES: biostudies-literature

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Brain Network Activation Analysis Utilizing Spatiotemporal Features for Event Related Potentials Classification.

Stern Yaki Y Reches Amit A Geva Amir B AB

Frontiers in computational neuroscience 20161220

The purpose of this study was to introduce an improved tool for automated classification of event-related potentials (ERPs) using spatiotemporally parcellated events incorporated into a functional brain network activation (BNA) analysis. The auditory oddball ERP paradigm was selected to demonstrate and evaluate the improved tool. <b>Methods:</b> The ERPs of each subject were decomposed into major dynamic spatiotemporal events. Then, a set of spatiotemporal events representing the group was gener ...[more]

PMID: 28066224

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Project description:ObjectiveThe purpose of this study is to explore the possibility of developing a biomarker that can discriminate early-stage Parkinson's disease from healthy brain function using electroencephalography (EEG) event-related potentials (ERPs) in combination with Brain Network Analytics (BNA) technology and machine learning (ML) algorithms.BackgroundCurrently, diagnosis of PD depends mainly on motor signs and symptoms. However, there is need for biomarkers that detect PD at an earlier stage to allow intervention and monitoring of potential disease-modifying therapies. Cognitive impairment may appear before motor symptoms, and it tends to worsen with disease progression. While ERPs obtained during cognitive tasks performance represent processing stages of cognitive brain functions, they have not yet been established as sensitive or specific markers for early-stage PD.MethodsNineteen PD patients (disease duration of ≤2 years) and 30 healthy controls (HC) underwent EEG recording while performing visual Go/No-Go and auditory Oddball cognitive tasks. ERPs were analyzed by the BNA technology, and a ML algorithm identified a combination of features that distinguish early PD from HC. We used a logistic regression classifier with a 10-fold cross-validation.ResultsThe ML algorithm identified a neuromarker comprising 15 BNA features that discriminated early PD patients from HC. The area-under-the-curve of the receiver-operating characteristic curve was 0.79. Sensitivity and specificity were 0.74 and 0.73, respectively. The five most important features could be classified into three cognitive functions: early sensory processing (P50 amplitude, N100 latency), filtering of information (P200 amplitude and topographic similarity), and response-locked activity (P-200 topographic similarity preceding the motor response in the visual Go/No-Go task).ConclusionsThis pilot study found that BNA can identify patients with early PD using an advanced analysis of ERPs. These results need to be validated in a larger PD patient sample and assessed for people with premotor phase of PD.

Project description:Previous research has pointed out that the combination of orthographic and semantic-associative training is a more advantageous strategy for the lexicalization of novel written word-forms than their single orthographic training. However, paradigms used previously involve explicit stimuli categorization (lexical decision), which likely influence word learning. In the present study, we used a more automatic task (silent reading) to determine the advantage of the associative training, by comparing the brain electrical signals elicited in combined (orthographic and semantic) and single (only orthographic) training conditions. In addition, the learning effect (in terms of similar neurophysiological activity between novel and known words) was also tested under a categorization paradigm, enabling determination of the possible influence of the training task in the lexicalization process. Results indicated that novel words repeatedly associated with meaningful cues showed a higher attenuation of N400 responses than those trained in the single orthographic condition, confirming the higher facilitation in the lexico-semantic processing of these stimuli, as a consequence of semantic associations. Moreover, only when the combined training was carried out in the reading task did novel words show similar N400 responses to those elicited by known words, suggesting the achievement of a similar lexical processing to known words. Crucially, when the training is carried out under a demanding task context (lexical decision), known words exhibited positive enhancement within the N400 time window, contributing to maintaining N400 differences with novel trained words and confounding the outcome of the learning. Such deflection-compatible with the modulation of the categorization-related P300 component-suggests that novel word learning could be influenced by the activation of categorization-related processes. Thus, the use of low-demand tasks arises as a more appropriate approach to study novel word learning, enabling the build-up process of mental representations, which probably depends on pure lexical and semantic factors rather than being guided by categorization demands.

Dataset Information

Brain Network Activation Analysis Utilizing Spatiotemporal Features for Event Related Potentials Classification.

Publications

Brain Network Activation Analysis Utilizing Spatiotemporal Features for Event Related Potentials Classification.

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