Multivariate Pattern Analysis of Electroencephalography Data in a Demand-Selection Task

2019 ◽  
pp. 403-411 ◽  
Author(s):  
David López-García ◽  
Alberto Sobrado ◽  
J. M. González-Peñalver ◽  
Juan Manuel Górriz ◽  
María Ruz
Keyword(s):  
Pattern Analysis ◽  
Selection Task ◽  
Multivariate Pattern Analysis ◽  
Multivariate Pattern ◽  
Demand Selection

IC-P-021: MULTIVARIATE PATTERN ANALYSIS ON A LONGITUDINAL COHORT OF COGNITIVELY NORMAL ELDERLY REVEALS DISTINCT STAGES OF REGIONAL AMYLOID DEPOSITION

2006 ◽  
Vol 14 (7S_Part_1) ◽  
pp. P26-P28 ◽  
Author(s):  
Aristeidis Sotiras ◽  
Murat Bilgel ◽  
Guray Erus ◽  
Yin Jie Chen ◽  
Mohamad Habes ◽  
...  
Keyword(s):  
Pattern Analysis ◽  
Amyloid Deposition ◽  
Multivariate Pattern Analysis ◽  
Cognitively Normal ◽  
Longitudinal Cohort ◽  
Multivariate Pattern

scholarly journals Multivariate Pattern Analysis of Electroencephalography Data Reveals Information Predictive of Charitable Giving

NeuroImage ◽  
2021 ◽  
pp. 118475
Author(s):  
Qiuyan Huang ◽  
Danyang Li ◽  
Can Zhou ◽  
Qiang Xu ◽  
Peng Li ◽  
...  
Keyword(s):  
Charitable Giving ◽  
Pattern Analysis ◽  
Multivariate Pattern Analysis ◽  
Multivariate Pattern

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.

scholarly journals Outcome contingency selectively affects the neural coding of outcomes but not of tasks

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
David Wisniewski ◽  
Birte Forstmann ◽  
Marcel Brass
Keyword(s):  
Decision Making ◽  
Parietal Cortex ◽  
Neural Coding ◽  
Free Choice ◽  
Pattern Analysis ◽  
Maintenance Phase ◽  
Large Network ◽  
Multivariate Pattern Analysis ◽  
Multivariate Pattern

AbstractValue-based decision-making is ubiquitous in every-day life, and critically depends on the contingency between choices and their outcomes. Only if outcomes are contingent on our choices can we make meaningful value-based decisions. Here, we investigate the effect of outcome contingency on the neural coding of rewards and tasks. Participants performed a reversal-learning paradigm in which reward outcomes were contingent on trial-by-trial choices, and performed a ‘free choice’ paradigm in which rewards were random and not contingent on choices. We hypothesized that contingent outcomes enhance the neural coding of rewards and tasks, which was tested using multivariate pattern analysis of fMRI data. Reward outcomes were encoded in a large network including the striatum, dmPFC and parietal cortex, and these representations were indeed amplified for contingent rewards. Tasks were encoded in the dmPFC at the time of decision-making, and in parietal cortex in a subsequent maintenance phase. We found no evidence for contingency-dependent modulations of task signals, demonstrating highly similar coding across contingency conditions. Our findings suggest selective effects of contingency on reward coding only, and further highlight the role of dmPFC and parietal cortex in value-based decision-making, as these were the only regions strongly involved in both reward and task coding.

scholarly journals The next step in modern brain lesion analysis: multivariate pattern analysis

Brain ◽  
2014 ◽  
Vol 137 (9) ◽  
pp. 2405-2407 ◽  
Author(s):  
Hans-Otto Karnath ◽  
David V. Smith
Keyword(s):  
Pattern Analysis ◽  
Brain Lesion ◽  
Multivariate Pattern Analysis ◽  
Lesion Analysis ◽  
Multivariate Pattern
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