scholarly journals Computational approaches to the neuroscience of social perception

2020 ◽  
Author(s):  
Jeffrey A Brooks ◽  
Ryan M Stolier ◽  
Jonathan B Freeman
Keyword(s):  
Social Perception ◽  
Impression Formation ◽  
Social Information ◽  
Brain Regions ◽  
Neural Representation ◽  
Fmri Data ◽  
Psychological Models ◽  
Fmri Analysis ◽  
Multivariate Pattern ◽  
Multiple Domains

Abstract Across multiple domains of social perception - including social categorization, emotion perception, impression formation, and mentalizing - multivariate pattern analysis (MVPA) of fMRI data has permitted a more detailed understanding of how social information is processed and represented in the brain. As in other neuroimaging fields, the neuroscientific study of social perception initially relied on broad structure-function associations derived from univariate fMRI analysis to map neural regions involved in these processes. In this review, we trace the ways that social neuroscience studies using MVPA have built on these neuroanatomical associations to better characterize the computational relevance of different brain regions, and how MVPA allows explicit tests of the correspondence between psychological models and the neural representation of social information. We also describe current and future advances in methodological approaches to multivariate fMRI data and their theoretical value for the neuroscience of social perception.

scholarly journals Intersubject MVPD: Empirical Comparison of fMRI Denoising Methods for Connectivity Analysis

2018 ◽  
Author(s):  
Yichen Li ◽  
Rebecca Saxe ◽  
Stefano Anzellotti
Keyword(s):  
Brain Region ◽  
Simulated Data ◽  
Ground Truth ◽  
Real Data ◽  
Brain Regions ◽  
Fmri Data ◽  
Statistical Dependence ◽  
The Real ◽  
The Difference ◽  
Multivariate Pattern

AbstractNoise is a major challenge for the analysis of fMRI data in general and for connectivity analyses in particular. As researchers develop increasingly sophisticated tools to model statistical dependence between the fMRI signal in different brain regions, there is a risk that these models may increasingly capture artifactual relationships between regions, that are the result of noise. Thus, choosing optimal denoising methods is a crucial step to maximize the accuracy and reproducibility of connectivity models. Most comparisons between denoising methods require knowledge of the ground truth: of what is the ‘real signal’. For this reason, they are usually based on simulated fMRI data. However, simulated data may not match the statistical properties of real data, limiting the generalizability of the conclusions. In this article, we propose an approach to evaluate denoising methods using real (non-simulated) fMRI data. First, we introduce an intersubject version of multivariate pattern dependence (iMVPD) that computes the statistical dependence between a brain region in one participant, and another brain region in a different participant. iMVPD has the following advantages: 1) it is multivariate, 2) it trains and tests models on independent folds of the real fMRI data, and 3) it generates predictions that are both between subjects and between regions. Since whole-brain sources of noise are more strongly correlated within subject than between subjects, we can use the difference between standard MVPD and iMVPD as a ‘discrepancy metric’ to evaluate denoising techniques (where more effective techniques should yield smaller differences). As predicted, the difference is the greatest in the absence of denoising methods. Furthermore, a combination of removal of the global signal and CompCorr optimizes denoising (among the set of denoising options tested).

scholarly journals Microcanonical and Canonical Ensembles for fMRI Brain Networks in Alzheimer’s Disease

Entropy ◽  
2021 ◽  
Vol 23 (2) ◽  
pp. 216 ◽  
Author(s):  
Jianjia Wang ◽  
Xichen Wu ◽  
Mingrui Li ◽  
Hui Wu ◽  
Edwin Hancock
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Ensemble Methods ◽  
Brain Regions ◽  
Fmri Data ◽  
Thermal Systems ◽  
Network Construction ◽  
Network Characteristics ◽  
Network Entropy ◽  
Canonical Ensembles

This paper seeks to advance the state-of-the-art in analysing fMRI data to detect onset of Alzheimer’s disease and identify stages in the disease progression. We employ methods of network neuroscience to represent correlation across fMRI data arrays, and introduce novel techniques for network construction and analysis. In network construction, we vary thresholds in establishing BOLD time series correlation between nodes, yielding variations in topological and other network characteristics. For network analysis, we employ methods developed for modelling statistical ensembles of virtual particles in thermal systems. The microcanonical ensemble and the canonical ensemble are analogous to two different fMRI network representations. In the former case, there is zero variance in the number of edges in each network, while in the latter case the set of networks have a variance in the number of edges. Ensemble methods describe the macroscopic properties of a network by considering the underlying microscopic characterisations which are in turn closely related to the degree configuration and network entropy. When applied to fMRI data in populations of Alzheimer’s patients and controls, our methods demonstrated levels of sensitivity adequate for clinical purposes in both identifying brain regions undergoing pathological changes and in revealing the dynamics of such changes.

scholarly journals Depression Disorder Classification of fMRI Data Using Sparse Low-Rank Functional Brain Network and Graph-Based Features

2017 ◽  
Vol 2017 ◽  
pp. 1-11 ◽  
Author(s):  
Xin Wang ◽  
Yanshuang Ren ◽  
Wensheng Zhang
Keyword(s):  
Pearson Correlation ◽  
Brain Regions ◽  
Brain Network ◽  
Classification Performance ◽  
Low Rank ◽  
Fmri Data ◽  
Healthy Controls ◽  
Functional Brain ◽  
Functional Brain Network ◽  
Depression Disorder

Study of functional brain network (FBN) based on functional magnetic resonance imaging (fMRI) has proved successful in depression disorder classification. One popular approach to construct FBN is Pearson correlation. However, it only captures pairwise relationship between brain regions, while it ignores the influence of other brain regions. Another common issue existing in many depression disorder classification methods is applying only single local feature extracted from constructed FBN. To address these issues, we develop a new method to classify fMRI data of patients with depression and healthy controls. First, we construct the FBN using a sparse low-rank model, which considers the relationship between two brain regions given all the other brain regions. Moreover, it can automatically remove weak relationship and retain the modular structure of FBN. Secondly, FBN are effectively measured by eight graph-based features from different aspects. Tested on fMRI data of 31 patients with depression and 29 healthy controls, our method achieves 95% accuracy, 96.77% sensitivity, and 93.10% specificity, which outperforms the Pearson correlation FBN and sparse FBN. In addition, the combination of graph-based features in our method further improves classification performance. Moreover, we explore the discriminative brain regions that contribute to depression disorder classification, which can help understand the pathogenesis of depression disorder.

scholarly journals Subjective value and decision entropy are jointly encoded by aligned gradients across the human brain

2020 ◽  
Author(s):  
Sebastian Bobadilla-Suarez ◽  
Olivia Guest ◽  
Bradley C. Love
Keyword(s):  
Prefrontal Cortex ◽  
Human Brain ◽  
Ventromedial Prefrontal Cortex ◽  
Neural Representation ◽  
Retrospective Evaluation ◽  
Systematic Fashion ◽  
Subjective Value ◽  
Fmri Analysis ◽  
The Relationship ◽  
The Brain

AbstractRecent work has considered the relationship between value and confidence in both behavior and neural representation. Here we evaluated whether the brain organizes value and confidence signals in a systematic fashion that reflects the overall desirability of options. If so, regions that respond to either increases or decreases in both value and confidence should be widespread. We strongly confirmed these predictions through a model-based fMRI analysis of a mixed gambles task that assessed subjective value (SV) and inverse decision entropy (iDE), which is related to confidence. Purported value areas more strongly signalled iDE than SV, underscoring how intertwined value and confidence are. A gradient tied to the desirability of actions transitioned from positive SV and iDE in ventromedial prefrontal cortex to negative SV and iDE in dorsal medial prefrontal cortex. This alignment of SV and iDE signals could support retrospective evaluation to guide learning and subsequent decisions.

scholarly journals Emotional contagion of pain across different social cues shares common and process-specific neural representations

2020 ◽  
Author(s):  
Feng Zhou ◽  
Jialin Li ◽  
Weihua Zhao ◽  
Lei Xu ◽  
Xiaoxiao Zheng ◽  
...  
Keyword(s):  
Anterior Cingulate Cortex ◽  
Emotional Contagion ◽  
Social Cues ◽  
Thermal Stimulation ◽  
Anterior Cingulate ◽  
Fmri Data ◽  
Whole Brain ◽  
Neural Representations ◽  
Experienced Pain ◽  
Multivariate Pattern

AbstractInsular and anterior cingulate cortex activation across vicarious pain induction procedures suggests that they are core pain empathy nodes. However, pain empathic responses encompass emotional contagion as well as unspecific arousal and overlapping functional activations are not sufficient to determine shared and process-specific neural representations. We employed multivariate pattern analyses to fMRI data acquired during physical and affective vicarious pain induction and found spatially and functionally similar cross-modality (physical versus affective) whole-brain vicarious pain-predictive patterns. Further analyses consistently identified shared neural representations in the bilateral mid-insula. Mid-insula vicarious pain patterns were not sensitive to capture non-painful arousing negative stimuli but predicted self-experienced pain during thermal stimulation, suggesting process-specific representation of emotional contagion for pain. Finally, a domain-general vicarious pain pattern which predicted vicarious as well as self-experienced pain was developed. Our findings demonstrate a generalizable neural expression of vicarious pain and suggest that the mid-insula encodes emotional contagion for pain.

scholarly journals The role of morality in social cognition

2019 ◽  
Author(s):  
Jennifer Lauren Ray ◽  
Peter Mende-Siedlecki ◽  
Ana P. Gantman ◽  
Jay Joseph Van Bavel
Keyword(s):  
Social Cognition ◽  
Social Perception ◽  
Impression Formation ◽  
Factor Models ◽  
The Other ◽  
One Dimension ◽  
Primary Role ◽  
The Past ◽  
Important Basis

Over the past few decades, two-factor models of social cognition have emerged as the dominant framework for understanding impression formation. Despite the differences in the labels, there is wide agreement that one dimension reflects sociability potential, and the other, competence. One way in which the various two-factor models do clearly differ, however, is in the way the dimensions incorporate or produce evaluations of morality. Aristotle saw morality as the most important basis on which to form positive evaluations, because competence and sociability could only be virtuous, sincere, and trustworthy if expressed through a moral character. This chapter highlights research demonstrating the unique and possibly primary role of morality in social cognition. We clarify the dynamic, interactive, and conjoint effects of morality on social perception, and argue morality, competence, and sociability are three influential and interactive dimensions of social perception.

scholarly journals Resting-state brain activity can predict target-independent aptitude in fMRI-neurofeedback training

2021 ◽  
Author(s):  
Takashi Nakano ◽  
Masahiro Takamura ◽  
Haruki Nishimura ◽  
Maro Machizawa ◽  
Naho Ichikawa ◽  
...  
Keyword(s):  
Resting State ◽  
Brain Connectivity ◽  
Brain Activity ◽  
Resting State Fmri ◽  
Brain Regions ◽  
Posterior Cingulate Cortex ◽  
Fmri Data ◽  
Independent Test ◽  
The Individual ◽  
The Brain

AbstractNeurofeedback (NF) aptitude, which refers to an individual’s ability to change its brain activity through NF training, has been reported to vary significantly from person to person. The prediction of individual NF aptitudes is critical in clinical NF applications. In the present study, we extracted the resting-state functional brain connectivity (FC) markers of NF aptitude independent of NF-targeting brain regions. We combined the data in fMRI-NF studies targeting four different brain regions at two independent sites (obtained from 59 healthy adults and six patients with major depressive disorder) to collect the resting-state fMRI data associated with aptitude scores in subsequent fMRI-NF training. We then trained the regression models to predict the individual NF aptitude scores from the resting-state fMRI data using a discovery dataset from one site and identified six resting-state FCs that predicted NF aptitude. Next we validated the prediction model using independent test data from another site. The result showed that the posterior cingulate cortex was the functional hub among the brain regions and formed predictive resting-state FCs, suggesting NF aptitude may be involved in the attentional mode-orientation modulation system’s characteristics in task-free resting-state brain activity.

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