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Liu M, Amey RC, Backer RA, Simon JP, Forbes CE. Behavioral Studies Using Large-Scale Brain Networks – Methods and Validations. Front Hum Neurosci 2022; 16:875201. [PMID: 35782044 PMCID: PMC9244405 DOI: 10.3389/fnhum.2022.875201] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2022] [Accepted: 05/17/2022] [Indexed: 11/13/2022] Open
Abstract
Mapping human behaviors to brain activity has become a key focus in modern cognitive neuroscience. As methods such as functional MRI (fMRI) advance cognitive scientists show an increasing interest in investigating neural activity in terms of functional connectivity and brain networks, rather than activation in a single brain region. Due to the noisy nature of neural activity, determining how behaviors are associated with specific neural signals is not well-established. Previous research has suggested graph theory techniques as a solution. Graph theory provides an opportunity to interpret human behaviors in terms of the topological organization of brain network architecture. Graph theory-based approaches, however, only scratch the surface of what neural connections relate to human behavior. Recently, the development of data-driven methods, e.g., machine learning and deep learning approaches, provide a new perspective to study the relationship between brain networks and human behaviors across the whole brain, expanding upon past literatures. In this review, we sought to revisit these data-driven approaches to facilitate our understanding of neural mechanisms and build models of human behaviors. We start with the popular graph theory approach and then discuss other data-driven approaches such as connectome-based predictive modeling, multivariate pattern analysis, network dynamic modeling, and deep learning techniques that quantify meaningful networks and connectivity related to cognition and behaviors. Importantly, for each topic, we discuss the pros and cons of the methods in addition to providing examples using our own data for each technique to describe how these methods can be applied to real-world neuroimaging data.
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Affiliation(s)
- Mengting Liu
- School of Biomedical Engineering, Sun Yat-sen University, Shenzhen, China
- Mengting Liu,
| | - Rachel C. Amey
- Department of Psychological and Brain Sciences, University of Delaware, Newark, DE, United States
- *Correspondence: Rachel C. Amey,
| | - Robert A. Backer
- Department of Psychological and Brain Sciences, University of Delaware, Newark, DE, United States
| | - Julia P. Simon
- Keck School of Medicine, University of Southern California, Los Angeles, CA, United States
| | - Chad E. Forbes
- Department of Psychology, Florida Atlantic University, Boca Raton, FL, United States
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Liu M, Backer RA, Amey RC, Forbes CE. How the brain negotiates divergent executive processing demands: Evidence of network reorganization in fleeting brain states. Neuroimage 2021; 245:118653. [PMID: 34688896 DOI: 10.1016/j.neuroimage.2021.118653] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2021] [Revised: 09/14/2021] [Accepted: 10/11/2021] [Indexed: 10/20/2022] Open
Abstract
During performance in everyday contexts, multiple networks draw from shared executive resources to maintain attention, regulate arousal, and solve problems. At times, requirements for attention and self-regulation appear to be in competition. How does the brain attempt to resolve conflicts arising from such divergent processing demands? Here we demonstrate that the brain is capable of managing multiple processes via rapidly cycling between functional brain states over time, as it is typically regarded. Treating the brain as a complex system, comprising relationships within and between functional networks, we implemented Hidden Markov Modeling (HMM) on electroencephalographic (EEG) data to identify nonlinear brain states in both intra and internetwork synchrony that produced better performance for women subjects who were tasked with solving difficult problems under autobiographically-relevant, evaluative stress. Prior work often found that emotion-regulation and default-mode network (ERN and DMN) activity conflicted with the frontoparietal network's (FPN) ability to facilitate executive functioning necessary for problem solving. Contrastingly, we discovered that fleeting, nonlinear states dominated by FPN and ERN internetwork synchrony supported optimum performance generally, while during stress, states dominated by ERN and DMN intranetwork synchrony were more important for performance. These results imply that the brain may be capable of resolving competing processes through networks' cooperative dynamics. Further, data suggests a novel role for DMN as a mechanism for integrating external threats with internal, self-referent processing during evaluative stress within the observed population.
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Affiliation(s)
- Mengting Liu
- Department of Psychological and Brain Sciences, University of Delaware, Newark, DE, USA; USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, University of Southern California, Los Angeles, CA, USA.
| | - Robert A Backer
- Department of Psychological and Brain Sciences, University of Delaware, Newark, DE, USA
| | - Rachel C Amey
- Department of Psychological and Brain Sciences, University of Delaware, Newark, DE, USA; Army Research Institute for the Behavioral and Social Sciences, Fort Belvoir, VA, USA
| | - Chad E Forbes
- Department of Psychological and Brain Sciences, University of Delaware, Newark, DE, USA
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Liu M, Backer RA, Amey RC, Splan EE, Magerman A, Forbes CE. Context Matters: Situational Stress Impedes Functional Reorganization of Intrinsic Brain Connectivity during Problem-Solving. Cereb Cortex 2020; 31:2111-2124. [PMID: 33251535 DOI: 10.1093/cercor/bhaa349] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2020] [Revised: 10/14/2020] [Accepted: 10/22/2020] [Indexed: 12/22/2022] Open
Abstract
Extensive research has established a relationship between individual differences in brain activity in a resting state and individual differences in behavior. Conversely, when individuals are engaged in various tasks, certain task-evoked reorganization occurs in brain functional connectivity, which can consequently influence individuals' performance as well. Here, we show that resting state and task-dependent state brain patterns interact as a function of contexts engendering stress. Findings revealed that when the resting state connectome was examined during performance, the relationship between connectome strength and performance only remained for participants under stress (who also performed worse than all other groups on the math task), suggesting that stress preserved brain patterns indicative of underperformance whereas non-stressed individuals spontaneously transitioned out of these patterns. Results imply that stress may impede the reorganization of a functional network in task-evoked brain states. This hypothesis was subsequently verified using graph theory measurements on a functional network, independent of behavior. For participants under stress, the functional network showed less topological alterations compared to non-stressed individuals during the transition from resting state to task-evoked state. Implications are discussed for network dynamics as a function of context.
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Affiliation(s)
- Mengting Liu
- Department of Psychological and Brain Sciences, University of Delaware, Newark, DE, USA.,USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, University of Southern California, Los Angeles, CA 90033, USA
| | - Robert A Backer
- Department of Psychological and Brain Sciences, University of Delaware, Newark, DE, USA
| | - Rachel C Amey
- Department of Psychological and Brain Sciences, University of Delaware, Newark, DE, USA
| | - Eric E Splan
- Department of Psychological and Brain Sciences, University of Delaware, Newark, DE, USA
| | - Adam Magerman
- Department of Psychological and Brain Sciences, University of Delaware, Newark, DE, USA
| | - Chad E Forbes
- Department of Psychological and Brain Sciences, University of Delaware, Newark, DE, USA
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Jin D, Li R, Xu J. Multiscale Community Detection in Functional Brain Networks Constructed Using Dynamic Time Warping. IEEE Trans Neural Syst Rehabil Eng 2019; 28:52-61. [PMID: 31634138 DOI: 10.1109/tnsre.2019.2948055] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
Previous studies have focused on the detection of community structures of brain networks constructed with resting-state functional magnetic resonance imaging (fMRI) data. Pearson correlation is often used to describe the connections between nodes in the construction of functional brain networks, which typically ignores the inherent timing and validity of fMRI time series. To solve this problem, this study applied the Dynamic Time Warp (DTW) algorithm to determine the correlation between two brain regions by comparing the synchronization and asynchrony of the time series. In addition, to determine the best community structure for each subject, we further divided the brain network into different scales, and then detected the different communities in these brain networks by using Modularity, Variation of Information (VI) and Normalized Mutual Information (NMI) as structural monitoring variables. Finally, we affirmed each subject's best community structure based on them. The experiments showed that through the method proposed in this paper, we not only accurately discovered important components of seven basic functional subnetworks, but also found that the putamen and Heschl's gyrus have a relationship with the inferior parietal network. Most importantly, this method can also determine each subject's functional brain network density, thus confirming the findings of studies testing real brain networks.
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Forbes CE, Amey R, Magerman AB, Duran K, Liu M. Stereotype-based stressors facilitate emotional memory neural network connectivity and encoding of negative information to degrade math self-perceptions among women. Soc Cogn Affect Neurosci 2018; 13:719-740. [PMID: 29939344 PMCID: PMC6121152 DOI: 10.1093/scan/nsy043] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2017] [Accepted: 06/18/2018] [Indexed: 12/03/2022] Open
Abstract
Stress engendered by stereotype threatening situations may facilitate encoding of negative, stereotype confirming feedback received during a performance among women in science, technology, engineering and mathematics (STEM). It is unclear, however, whether this process is comprised of the same neurophysiological mechanisms evident in any emotional memory encoding context, or if this encoding bias directly undermines positive self-perceptions in the stigmatized domain. A total of 160 men and women completed a math test that provided veridical positive and negative feedback, a memory test for feedback, and math self-enhancing and valuing measures in a stereotype threatening or neutral context while continuous electroencephalography activity and startle probe responses to positive and negative feedback was recorded. Indexing amygdala activity to feedback via startle responses and emotional memory network connectivity elicited during accurate recognition of positive and negative feedback via graph analyses, only stereotype threatened women encoded negative feedback better when they exhibited increased amygdala activity and emotional memory network connectivity in response to said feedback. Emotional memory biases, in turn, predicted decreases in women’s self-enhancing, math valuing and performance. Findings provide an emotional memory encoding-based mechanism for well-established findings indicating that women have more negative math self-perceptions compared with men regardless of actual performance.
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Affiliation(s)
- Chad E Forbes
- Social Neuroscience Laboratory, Department of Psychological and Brain Sciences, University of Delaware, Newark, DE 19716, USA
| | - Rachel Amey
- Social Neuroscience Laboratory, Department of Psychological and Brain Sciences, University of Delaware, Newark, DE 19716, USA
| | - Adam B Magerman
- Social Neuroscience Laboratory, Department of Psychological and Brain Sciences, University of Delaware, Newark, DE 19716, USA
| | - Kelly Duran
- Social Neuroscience Laboratory, Department of Psychological and Brain Sciences, University of Delaware, Newark, DE 19716, USA
| | - Mengting Liu
- Social Neuroscience Laboratory, Department of Psychological and Brain Sciences, University of Delaware, Newark, DE 19716, USA
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