A multivariate pattern analysis of resting-state functional MRI data in Naïve and chronic betel quid chewers

2020 ◽  
Author(s):  
Zeqiang Linli ◽  
Xiaojun Huang ◽  
Zhening Liu ◽  
Shuixia Guo ◽  
Adellah Sariah
Keyword(s):  
Functional Mri ◽  
Resting State ◽  
Pattern Analysis ◽  
Betel Quid ◽  
Multivariate Pattern Analysis ◽  
Multivariate Pattern

scholarly journals Guilty by association: How group-based (collective) guilt arises in the brain

2019 ◽  
Author(s):  
Zhiai Li ◽  
Hongbo Yu ◽  
Yongdi Zhou ◽  
Tobias Kalenscher ◽  
Xiaolin Zhou
Keyword(s):  
Functional Mri ◽  
Pattern Analysis ◽  
Group Membership ◽  
Shared Responsibility ◽  
Multivariate Pattern Analysis ◽  
Collective Guilt ◽  
Social Emotions ◽  
Group Members ◽  
Multivariate Pattern ◽  
The Brain

AbstractPeople do not only feel guilty for transgressions of social norms/expectations that they are causally responsible for, but they also feel guilty for transgressions committed by those they identify as in-group (i.e., collective or group-based guilt). However, the neurocognitive basis of group-based guilt and its relation to personal guilt are unknown. To address these questions, we combined functional MRI with an interaction-based minimal group paradigm in which participants either directly caused harm to victims (i.e., personal guilt), or observed in-group members cause harm to the victims (i.e., group-based guilt). In three experiments (N = 90), we demonstrated that perceived shared responsibility with in-group members in the transgression predicted behavioral and neural manifestations of group-based guilt. Multivariate pattern analysis of the functional MRI data showed that group-based guilt recruited a similar brain representation in anterior middle cingulate cortex as personal guilt. These results have broaden our understanding of how group membership is integrated into social emotions.

scholarly journals Elucidating the neural circuitry underlying planning of internally-guided voluntary action

2016 ◽  
Vol 116 (6) ◽  
pp. 2469-2472
Author(s):  
Michelle Marneweck ◽  
Véronique H. Flamand
Keyword(s):  
Functional Mri ◽  
Decision Model ◽  
Pattern Analysis ◽  
Action Planning ◽  
Neural Circuitry ◽  
Voluntary Action ◽  
Action Plans ◽  
Multivariate Pattern Analysis ◽  
Action Type ◽  
Multivariate Pattern

In an attempt to elucidate the neural circuitry of planning of internally guided voluntary action, Ariani et al. (2015) used a delayed-movement design and multivariate pattern analysis of functional MRI data and found areas decoding internally elicited action plans, stimulus-elicited action plans, and both types of plans. In interpreting their results in the context of a heuristic decision model of voluntary action, encompassing “what” action to perform, “when” to perform it, and “whether” to perform it at all, we highlight at least some neural dissociation of these components. More to that, we note that the exact neural circuitry of each component might vary depending on the performed action type, and finally, we underscore the importance of understanding the temporal specifics of such circuitries to further elucidate how they are involved and interact during voluntary action planning.

Altered Resting State Dynamics in Unmedicated First-Episode Schizophrenia: A Multivariate Pattern Analysis

2021 ◽  
Vol 89 (9) ◽  
pp. S258-S259
Author(s):  
Venkataram Shivakumar ◽  
Vanteemar S. Sreeraj ◽  
Sunil V. Kalmady ◽  
Gaurav V. Bhalerao ◽  
Anekal C. Amaresha ◽  
...  
Keyword(s):  
Resting State ◽  
Pattern Analysis ◽  
First Episode ◽  
Multivariate Pattern Analysis ◽  
Multivariate Pattern ◽  
First Episode Schizophrenia

scholarly journals Identifying Depressed Essential Tremor Using Resting-State Voxel-Wise Global Brain Connectivity: A Multivariate Pattern Analysis

2021 ◽  
Vol 15 ◽  
Author(s):  
Yufen Li ◽  
Li Tao ◽  
Huiyue Chen ◽  
Hansheng Wang ◽  
Xiaoyu Zhang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Prefrontal Cortex ◽  
Essential Tremor ◽  
Resting State ◽  
Pattern Analysis ◽  
Brain Connectivity ◽  
Support Vector ◽  
Multivariate Pattern Analysis ◽  
Multivariate Pattern ◽  
Global Brain

Background and Objective: Although depression is one of the most common non-motor symptoms in essential tremor (ET), its pathogenesis and diagnosis biomarker are still unknown. Recently, machine learning multivariate pattern analysis (MVPA) combined with connectivity mapping of resting-state fMRI has provided a promising way to identify patients with depressed ET at the individual level and help to reveal the brain network pathogenesis of depression in patients with ET.Methods: Based on global brain connectivity (GBC) mapping from 41 depressed ET, 49 non-depressed ET, 45 primary depression, and 43 healthy controls (HCs), multiclass Gaussian process classification (GPC) and binary support vector machine (SVM) algorithms were used to identify patients with depressed ET from non-depressed ET, primary depression, and HCs, and the accuracy and permutation tests were used to assess the classification performance.Results: While the total accuracy (40.45%) of four-class GPC was poor, the four-class GPC could discriminate depressed ET from non-depressed ET, primary depression, and HCs with a sensitivity of 70.73% (P < 0.001). At the same time, the sensitivity of using binary SVM to discriminate depressed ET from non-depressed ET, primary depression, and HCs was 73.17, 80.49, and 75.61%, respectively (P < 0.001). The significant discriminative features were mainly located in cerebellar-motor-prefrontal cortex circuits (P < 0.001), and a further correlation analysis showed that the GBC values of significant discriminative features in the right middle prefrontal gyrus, bilateral cerebellum VI, and Crus 1 were correlated with clinical depression severity in patients with depressed ET.Conclusion: Our findings demonstrated that GBC mapping combined with machine learning MVPA could be used to identify patients with depressed ET, and the GBC changes in cerebellar-prefrontal cortex circuits not only posed as the significant discriminative features but also helped to understand the network pathogenesis underlying depression in patients with ET.

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