scholarly journals Identifying major depression using whole-brain functional connectivity: a multivariate pattern analysis

Brain ◽  
2012 ◽  
Vol 135 (5) ◽  
pp. 1498-1507 ◽  
Author(s):  
Ling-Li Zeng ◽  
Hui Shen ◽  
Li Liu ◽  
Lubin Wang ◽  
Baojuan Li ◽  
...  
Keyword(s):  
Major Depression ◽  
Functional Connectivity ◽  
Pattern Analysis ◽  
Multivariate Pattern Analysis ◽  
Whole Brain ◽  
Multivariate Pattern ◽  
Brain Functional Connectivity

scholarly journals Multivariate Pattern Analysis of fMRI in Breast Cancer Survivors and Healthy Women

2013 ◽  
Vol 20 (4) ◽  
pp. 391-401 ◽  
Author(s):  
S.M. Hadi Hosseini ◽  
Shelli R. Kesler
Keyword(s):  
Breast Cancer ◽  
Functional Connectivity ◽  
Breast Cancer Survivors ◽  
Pattern Analysis ◽  
Brain Connectivity ◽  
Survival Rates ◽  
Connectivity Pattern ◽  
Multivariate Pattern Analysis ◽  
The Right ◽  
Multivariate Pattern

AbstractAdvances in breast cancer (BC) treatments have resulted in significantly improved survival rates. However, BC chemotherapy is often associated with several side effects including cognitive dysfunction. We applied multivariate pattern analysis (MVPA) to functional magnetic resonance imaging (fMRI) to find a brain connectivity pattern that accurately and automatically distinguishes chemotherapy-treated (C+) from non-chemotherapy treated (C−) BC females and healthy female controls (HC). Twenty-seven C+, 29 C−, and 30 HC underwent fMRI during an executive-prefrontal task (Go/Nogo). The pattern of functional connectivity associated with this task discriminated with significant accuracy between C+ and HC groups (72%, p = .006) and between C+ and C− groups (71%, p = .012). However, the accuracy of discrimination between C− and HC was not significant (51%, p = .46). Compared with HC, behavioral performance of the C+ and C− groups during the task was intact. However, the C+ group demonstrated altered functional connectivity in the right frontoparietal and left supplementary motor area networks compared to HC, and in the right middle frontal and left superior frontal gyri networks, compared to C−. Our results provide further evidence that executive function performance may be preserved in some chemotherapy-treated BC survivors through recruitment of additional neural connections. (JINS, 2013, 19, 1–11)

scholarly journals Cocaine dependence and thalamic functional connectivity: a multivariate pattern analysis

2016 ◽  
Vol 12 ◽  
pp. 348-358 ◽  
Author(s):  
Sheng Zhang ◽  
Sien Hu ◽  
Rajita Sinha ◽  
Marc N. Potenza ◽  
Robert T. Malison ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Pattern Analysis ◽  
Cocaine Dependence ◽  
Multivariate Pattern Analysis ◽  
Multivariate Pattern

scholarly journals Functional connectivity differences in early infancy precede autism symptoms: a multivariate pattern analysis

2019 ◽  
Author(s):  
Abigail Dickinson ◽  
Manjari Daniel ◽  
Andrew Marin ◽  
Bilwaj Goanker ◽  
Mirella Dapretto ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Cognitive Abilities ◽  
Pattern Analysis ◽  
Brain Connectivity ◽  
Familial Risk ◽  
Autism Spectrum ◽  
Support Vector ◽  
Multivariate Pattern Analysis ◽  
Autism Symptoms ◽  
Multivariate Pattern

AbstractFunctional brain connectivity is altered in children and adults with autism spectrum disorder (ASD). Mapping pre-symptomatic functional disruptions in ASD could identify infants based on neural risk, providing a crucial opportunity to mediate outcomes before behavioral symptoms emerge.Here we quantify functional connectivity using scalable EEG measures of oscillatory phase coherence (6-12Hz). Infants at high and low familial risk for ASD (N=65) underwent an EEG recording at 3 months of age and were assessed for ASD symptoms at 18 months using the Autism Diagnostic Observation Schedule-Toddler Module. Multivariate pattern analysis was used to examine early functional patterns that are associated with later ASD symptoms.Support vector regression (SVR) algorithms accurately predicted observed ASD symptoms at 18 months from EEG data at 3 months (r=0.76, p=0.02). Specifically, lower frontal connectivity and higher right temporo-parietal connectivity predicted higher ASD symptoms. The SVR model did not predict non-verbal cognitive abilities at 18 months (r=0.15, p=0.36), suggesting specificity of these brain alterations to ASD.These data suggest that frontal and temporo-parietal dysconnectivity play important roles in the early pathophysiology of ASD. Early functional differences in ASD can be captured using EEG during infancy and may inform much-needed advancements in the early detection of ASD.

scholarly journals Multivariate Pattern Analysis of EEG-Based Functional Connectivity: A Study on the Identification of Depression

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 92630-92641 ◽  
Author(s):  
Hong Peng ◽  
Chen Xia ◽  
Zihan Wang ◽  
Jing Zhu ◽  
Xin Zhang ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Pattern Analysis ◽  
Multivariate Pattern Analysis ◽  
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.

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