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Positive Classification Advantage: Tracing the Time Course Based on Brain Oscillation.

ABSTRACT: The present study aimed to explore the modulation of frequency bands (alpha, beta, theta) underlying the positive facial expressions classification advantage within different post-stimulus time intervals (100-200 ms, 200-300 ms, 300-400 ms). For this purpose, we recorded electroencephalogram (EEG) activity during an emotion discrimination task for happy, sad and neutral faces. The correlation between the non-phase-locked power of frequency bands and reaction times (RTs) was assessed. The results revealed that beta played a major role in positive classification advantage (PCA) within the 100-200 and 300-400 ms intervals, whereas theta was important within the 200-300 ms interval. We propose that the beta band modulated the neutral and emotional face classification process, and that the theta band modulated for happy and sad face classification.

SUBMITTER: Yan T

PROVIDER: S-EPMC5768652 | biostudies-literature | 2017

REPOSITORIES: biostudies-literature

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Positive Classification Advantage: Tracing the Time Course Based on Brain Oscillation.

Yan Tianyi T Dong Xiaonan X Mu Nan N Liu Tiantian T Chen Duanduan D Deng Li L Wang Changming C Zhao Lun L

Frontiers in human neuroscience 20180111

The present study aimed to explore the modulation of frequency bands (alpha, beta, theta) underlying the positive facial expressions classification advantage within different post-stimulus time intervals (100-200 ms, 200-300 ms, 300-400 ms). For this purpose, we recorded electroencephalogram (EEG) activity during an emotion discrimination task for happy, sad and neutral faces. The correlation between the non-phase-locked power of frequency bands and reaction times (RTs) was assessed. The results ...[more]

PMID: 29375353

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Project description:BackgroundThis study aimed to develop a classification system for lumbar disc degeneration using routine magnetic resonance images (MRIs) that is easily applicable and unaffected by existing classifications' limitations, and to compare its reliability, reproducibility, and discriminative power to the widely used Pfirrmann classification.MethodsFive features were graded. This new classification system has eight grades, with at least one of these five features altering each grade. The T2-weighted sagittal images were acquired using a rapid spin-echo sequence with a repetition time of 2680 to 4900 milliseconds, an echo time of 100 to 109 milliseconds, and an echo train length of 17. Slice thick was 4 mm and the display field of view was 32 × 32 cm. The new classification system used five features: signal intensity, disc height, disc boundary regularity, and nucleus annulus separation. Increased signal intensity, decreased height, decreased regularity, and decreased nucleus-annulus separation indicated degeneration. Four raters classified 400 discs from 80 patients using the Pfirrmann and Novel systems. Statistical analyses were conducted to investigate reliability and correlation.ResultsThe overall ICC and kappa values were found to be higher in the novel classification. (0.988 indicating excellent agreement for ICC and 0.76/0.94 indicating good-very good agreement for kappa). The Kendall tau c value, which shows the correlation between the two classifications and indicates the validity of the new classification, was 0.872, which is very strong. Through the use of cross-tabulations, the discriminatory power of the two newly added classification criteria was determined.ConclusionsThis study demonstrates the intra-rater and inter-rater reliability of an easy-to-use, discriminative novel morphometric MRI based classification system for lumbar disc degeneration. The differentiation of grades based on five distinct criteria may generate novel hypotheses regarding treatment selection and response monitoring, as well as new insights into the study of disc degeneration.

Dataset Information

Positive Classification Advantage: Tracing the Time Course Based on Brain Oscillation.

Publications

Positive Classification Advantage: Tracing the Time Course Based on Brain Oscillation.

Similar Datasets

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