scholarly journals Dissociating decisional and temporal information in interval categorisation

2019 ◽  
Author(s):  
Vanessa C. Morita ◽  
João R. Sato ◽  
Marcelo S. Caetano ◽  
André M. Cravo
Keyword(s):  
Evoked Potentials ◽  
Temporal Processing ◽  
Pattern Analysis ◽  
Interval Timing ◽  
Eeg Signal ◽  
Multivariate Pattern Analysis ◽  
Eeg Activity ◽  
Electrophysiological Recordings ◽  
Human Participants ◽  
Multivariate Pattern

AbstractInterval timing is fundamental for humans and non-human animals to interact with their environment. Several studies that investigate temporal processing combine behavioural tasks with neurophysiological methods, such as electrophysiological recordings (EEG). However, in the majority of these studies, it is hard to dissociate whether EEG activity reflects temporal or decisional information. In the present study, we investigated how time and decision is encoded in the EEG signal while human participants performed a temporal categorisation task with two different temporal references. Using a combination of evoked potentials and multivariate pattern analysis, we show that: (1) During the interval to-be-timed, both temporal and decisional information are encoded; (2) Activity evoked by the end of the interval encodes almost exclusively decisional information. These results suggest that decisional aspects of the task better explain EEG activity commonly related to temporal processing. The interplay between the encoding of time and decision is consistent with recent proposals that approximate temporal processing with decisional models.

scholarly journals A Distinct Role of the Temporal-Parietal Junction in Predicting Socially Guided Decisions

Science ◽  
2012 ◽  
Vol 337 (6090) ◽  
pp. 109-111 ◽  
Author(s):  
R. McKell Carter ◽  
Daniel L. Bowling ◽  
Crystal Reeck ◽  
Scott A. Huettel
Keyword(s):  
Pattern Analysis ◽  
Multivariate Pattern Analysis ◽  
Functional Magnetic Resonance ◽  
Resonance Imaging ◽  
Incentive Compatible ◽  
Future Behavior ◽  
Human Participants ◽  
Multivariate Pattern ◽  
Distinct Role

To make adaptive decisions in a social context, humans must identify relevant agents in the environment, infer their underlying strategies and motivations, and predict their upcoming actions. We used functional magnetic resonance imaging, in conjunction with combinatorial multivariate pattern analysis, to predict human participants’ subsequent decisions in an incentive-compatible poker game. We found that signals from the temporal-parietal junction provided unique information about the nature of the upcoming decision, and that information was specific to decisions against agents who were both social and relevant for future behavior.

scholarly journals Multivariate pattern analysis reveals location specific aftereffects of 10Hz motor cortex transcranial alternating current stimulation

2021 ◽  
Author(s):  
Elinor Tzvi ◽  
Jalal Alizadeh ◽  
Christine Schubert ◽  
Joseph Classen
Keyword(s):  
Motor Cortex ◽  
Primary Motor Cortex ◽  
Pattern Analysis ◽  
Premotor Cortex ◽  
Alternating Current ◽  
Eeg Signal ◽  
Multivariate Pattern Analysis ◽  
Eeg Signals ◽  
Transcranial Alternating Current Stimulation ◽  
Multivariate Pattern

Background: Transcranial alternating current stimulation (tACS) may induce frequency-specific aftereffects on brain oscillations in the stimulated location, which could serve as evidence for region-specific neuroplasticity. Aftereffects of tACS on the motor system remain unknown. Objective: To find evidence for aftereffects in short EEG segments following tACS to two critical nodes of the motor network, namely, left motor cortex (lMC) and right cerebellum (rCB). We hypothesized that aftereffects of lMC will be stronger in and around lMC compared to both active stimulation of rCB, as well as inactive (sham) control conditions. Methods: To this end, we employed multivariate pattern analysis (MVPA), and trained a classifier to distinguish between EEG signals following each of the three stimulation protocols. This method accounts for the multitude facets of the EEG signal and thus is more sensitive to subtle modulation of the EEG signal. Results: EEG signals in both theta (θ, 4-8Hz) and alpha (α, 8-13Hz) were better classified to lMC-tACS compared to rCB-tACS/sham, in and around lMC-tACS stimulation locations (electrodes FC3 and CP3). This effect was associated with a decrease in power following tACS. Source reconstruction revealed significant differences in premotor cortex but not in primary motor cortex as the computational model suggested. Correlation between classification accuracies in θ and α in lMC-tACS was stronger compared to rCB-tACS/sham, suggesting cross-frequency effects of tACS. Nonetheless, θ/α phase-coupling did not differ between stimulation protocols. Conclusions: Successful classification of EEG signals to left motor cortex using MVPA revealed focal tACS aftereffects on the motor cortex, indicative of region-specific neuroplasticity.

scholarly journals A Multivariate Pattern Analysis of Metabolic Profile in Neurologically Impaired Children and Adolescents

Children ◽  
2021 ◽  
Vol 8 (3) ◽  
pp. 186
Author(s):  
Valeria Calcaterra ◽  
Giacomo Biganzoli ◽  
Gloria Pelizzo ◽  
Hellas Cena ◽  
Alessandra Rizzuto ◽  
...  
Keyword(s):  
Metabolic Syndrome ◽  
Glucose Metabolism ◽  
Children And Adolescents ◽  
Metabolic Profile ◽  
Children With Disabilities ◽  
Pattern Analysis ◽  
Multivariate Pattern Analysis ◽  
Neurologically Impaired ◽  
Multivariate Pattern ◽  
Impaired Children

Background: The prevalence of pediatric metabolic syndrome is usually closely linked to overweight and obesity; however, this condition has also been described in children with disabilities. We performed a multivariate pattern analysis of metabolic profiles in neurologically impaired children and adolescents in order to reveal patterns and crucial biomarkers among highly interrelated variables. Patients and methods: We retrospectively reviewed 44 cases of patients (25M/19F, mean age 12.9 ± 8.0) with severe disabilities. Clinical and anthropometric parameters, body composition, blood pressure, and metabolic and endocrinological assessment (fasting blood glucose, insulin, total cholesterol, high-density lipoprotein cholesterol, triglycerides, glutamic oxaloacetic transaminase, glutamate pyruvate transaminase, gamma-glutamyl transpeptidase) were recorded in all patients. As a control group, we evaluated 120 healthy children and adolescents (61M/59F, mean age 12.9 ± 2.7). Results: In the univariate analysis, the children-with-disabilities group showed a more dispersed distribution, thus with higher variability of the features related to glucose metabolism and insulin resistance (IR) compared to the healthy controls. The principal component (PC1), which emerged from the PC analysis conducted on the merged dataset and characterized by these variables, was crucial in describing the differences between the children-with-disabilities group and controls. Conclusion: Children and adolescents with disabilities displayed a different metabolic profile compared to controls. Metabolic syndrome (MetS), particularly glucose metabolism and IR, is a crucial point to consider in the treatment and care of this fragile pediatric population. Early detection of the interrelated variables and intervention on these modifiable risk factors for metabolic disturbances play a central role in pediatric health and life expectancy in patients with a severe disability.

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