scholarly journals Author Correction: Multivariate pattern analysis of brain structure predicts functional outcome after auditory-based cognitive training interventions

2021 ◽  
Vol 7 (1) ◽  
Author(s):  
Lana Kambeitz-Ilankovic ◽  
Sophia Vinogradov ◽  
Julian Wenzel ◽  
Melissa Fisher ◽  
Shalaila S. Haas ◽  
...  
Keyword(s):  
Functional Outcome ◽  
Cognitive Training ◽  
Brain Structure ◽  
Pattern Analysis ◽  
Multivariate Pattern Analysis ◽  
Training Interventions ◽  
Multivariate Pattern

scholarly journals Multivariate pattern analysis of brain structure predicts functional outcome after auditory-based cognitive training interventions

2021 ◽  
Vol 7 (1) ◽  
Author(s):  
Lana Kambeitz-Ilankovic ◽  
Sophia Vinogradov ◽  
Julian Wenzel ◽  
Melissa Fisher ◽  
Shalaila S. Haas ◽  
...  
Keyword(s):  
Cognitive Training ◽  
Cross Validation ◽  
Pattern Analysis ◽  
Anterior Cingulate ◽  
Support Vector ◽  
Single Subject ◽  
Multivariate Pattern Analysis ◽  
Whole Brain ◽  
Training Interventions ◽  
Multivariate Pattern

AbstractCognitive gains following cognitive training interventions are associated with improved functioning in people with schizophrenia (SCZ). However, considerable inter-individual variability is observed. Here, we evaluate the sensitivity of brain structural features to predict functional response to auditory-based cognitive training (ABCT) at a single-subject level. We employed whole-brain multivariate pattern analysis with support vector machine (SVM) modeling to identify gray matter (GM) patterns that predicted higher vs. lower functioning after 40 h of ABCT at the single-subject level in SCZ patients. The generalization capacity of the SVM model was evaluated by applying the original model through an out-of-sample cross-validation analysis to unseen SCZ patients from an independent validation sample who underwent 50 h of ABCT. The whole-brain GM volume-based pattern classification predicted higher vs. lower functioning at follow-up with a balanced accuracy (BAC) of 69.4% (sensitivity 72.2%, specificity 66.7%) as determined by nested cross-validation. The neuroanatomical model was generalizable to an independent cohort with a BAC of 62.1% (sensitivity 90.9%, specificity 33.3%). In particular, greater baseline GM volumes in regions within superior temporal gyrus, thalamus, anterior cingulate, and cerebellum predicted improved functioning at the single-subject level following ABCT in SCZ participants. The present findings provide a structural MRI fingerprint associated with preserved GM volumes at a single baseline timepoint, which predicted improved functioning following an ABCT intervention, and serve as a model for how to facilitate precision clinical therapies for SCZ based on imaging data, operating at the single-subject level.

scholarly journals Multivariate pattern analysis of brain structure predicts functional outcome after auditory-based cognitive training interventions

2020 ◽  
Author(s):  
Lana Kambeitz-Ilankovic ◽  
Sophia Vinogradov ◽  
Julian Wenzel ◽  
Melissa Fisher ◽  
Shalaila S. Haas ◽  
...  
Keyword(s):  
Cognitive Training ◽  
Cross Validation ◽  
Pattern Analysis ◽  
Anterior Cingulate ◽  
Support Vector ◽  
Single Subject ◽  
Multivariate Pattern Analysis ◽  
Whole Brain ◽  
Training Interventions ◽  
Multivariate Pattern

AbstractBackgroundCognitive gains following cognitive training interventions (CT) are associated with improved functioning in people with schizophrenia (SCZ). However, considerable inter-individual variability is observed. Here, we evaluate the sensitivity of brain structural features to predict functional response to auditory-based cognitive training (ABCT) at a single subject level.MethodsWe employed whole-brain multivariate pattern analysis (MVPA) with support vector machine (SVM) modeling to identify grey matter (GM) patterns that predicted ‘higher’ vs. ‘lower’ functioning after 40 hours of ABCT at the single subject level in SCZ patients. The generalization capacity of the SVM model was evaluated by applying the original model through an Out-Of-Sample Cross Validation analysis (OOCV) to unseen SCZ patients from an independent sample that underwent 50 hours of ABCT.ResultsThe whole-brain GM volume-based pattern classification predicted ‘higher’ vs. ‘lower’ functioning at follow-up with a balanced accuracy (BAC) of 69.4% (sensitivity 72.2%, specificity 66.7%) as determined by nested cross-validation. The neuroanatomical model was generalizable to an independent cohort with a BAC of 62.1% (sensitivity 90.9%, specificity 33.3%).ConclusionsIn particular, greater baseline GM volume in regions within superior temporal gyrus, thalamus, anterior cingulate and cerebellum -- predicted improved functioning at the single-subject level following ABCT in SCZ participants.

scholarly journals Traces of Trauma: A Multivariate Pattern Analysis of Childhood Trauma, Brain Structure, and Clinical Phenotypes

2020 ◽  
Vol 88 (11) ◽  
pp. 829-842 ◽  
Author(s):  
David Popovic ◽  
Anne Ruef ◽  
Dominic B. Dwyer ◽  
Linda A. Antonucci ◽  
Julia Eder ◽  
...  
Keyword(s):  
Childhood Trauma ◽  
Brain Structure ◽  
Pattern Analysis ◽  
Multivariate Pattern Analysis ◽  
Clinical Phenotypes ◽  
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.

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