Computational exploration of dynamic mechanisms of steady state visual evoked potentials at the whole brain level

Zhang, Ge; Cui, Yan; Zhang, Yangsong; Cao, Hefei; Zhou, Guanyu; Shu, Hai-Feng; Yao, Dezhong; Yang, Xiaobo; Chen, Ke; Guo, Daqing

doi:10.1016/j.neuroimage.2021.118166

Cited by 21 publications

(10 citation statements)

References 72 publications

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“…Finally, although we demonstrated the association between brain networks and BD symptoms, the neural mechanism underlying these symptoms are still unclear. The large‐scale brain modeling is a powerful approach to analyze the dynamic mechanism and has been applied to brain disorders and cognition (Chen et al, 2015 ; Herzog et al, 2022 ; Zhang et al, 2021 ). The main findings that were identified in this study need to be further explained by building a large‐scale brain model of BD patients.…”

Section: Limitations Of the Studymentioning

confidence: 99%

Segregation, integration and balance in resting‐state brain functional networks associated with bipolar disorder symptoms

Chang

Wang

et al. 2022

Human Brain Mapping

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Bipolar disorder (BD) is a serious mental disorder involving widespread abnormal interactions between brain regions, and it is believed to be associated with imbalanced functions in the brain. However, how this brain imbalance underlies distinct BD symptoms remains poorly understood. Here, we used a nested‐spectral partition (NSP) method to study the segregation, integration, and balance in resting‐state brain functional networks in BD patients and healthy controls (HCs). We first confirmed that there was a high deviation in the brain functional network toward more segregation in BD patients than in HCs and that the limbic system had the largest alteration. Second, we demonstrated a network balance of segregation and integration that corresponded to lower anxiety in BD patients but was not related to other symptoms. Subsequently, based on a machine‐learning approach, we identified different system‐level mechanisms underlying distinct BD symptoms and found that the features related to the brain network balance could predict BD symptoms better than graph theory analyses. Finally, we studied attention‐deficit/hyperactivity disorder (ADHD) symptoms in BD patients and identified specific patterns that distinctly predicted ADHD and BD scores, as well as their shared common domains. Our findings supported an association of brain imbalance with anxiety symptom in BD patients and provided a potential network signature for diagnosing BD. These results contribute to further understanding the neuropathology of BD and to screening ADHD in BD patients.

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Section: Limitations Of the Studymentioning

confidence: 99%

Segregation, integration and balance in resting‐state brain functional networks associated with bipolar disorder symptoms

Chang

Wang

et al. 2022

Human Brain Mapping

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“…SSVEP signals mainly appear over parietal and occipital regions since they are closer to the visual cortex of the human brain [35]- [37]. Some studies presented that SSVEP signals near these areas have larger amplitude and SNR [34], [38]. Therefore, nine electrodes (i.e., Pz, PO3, POz, PO4, PO7, O1, Oz, O2, and PO8) located in parietal and occipital areas were used to record EEG signals.…”

Section: A Ssvep Datasetsmentioning

confidence: 99%

Cross-Subject Transfer Learning for Boosting Recognition Performance in SSVEP-Based BCIs

Zhang

Xie

Shi

et al. 2023

IEEE Trans. Neural Syst. Rehabil. Eng.

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Steady-state visual evoked potential (SSVEP)-based brain-computer interfaces (BCIs) have been substantially studied in recent years due to their fast communication rate and high signal-to-noise ratio. The transfer learning is typically utilized to improve the performance of SSVEP-based BCIs with auxiliary data from the source domain. This study proposed an intersubject transfer learning method for enhancing SSVEP recognition performance through transferred templates and transferred spatial filters. In our method, the spatial filter was trained via multiple covariance maximization to extract SSVEP-related information. The relationships between the training trial, the individual template, and the artificially constructed reference are involved in the training process. The spatial filters are applied to the above templates to form two new transferred templates, and the transferred spatial filters are obtained accordingly via the least-square regression. The contribution scores of different source subjects can be calculated based on the distance between the source subject and the target subject. Finally, a fourdimensional feature vector is constructed for SSVEP detection. To demonstrate the effectiveness of the proposed method, a publicly available dataset and a self-collected dataset were employed for performance evaluation. The extensive experimental results validated the feasibility of the proposed method for improving SSVEP detection.

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“…Similarly, the present thrust of whole-brain approaches is oriented toward modeling recordings while participants are not engaged in overt cognition, aka resting-state (Biswal et al, 1995 ; Deco et al, 2011 ; Popovych et al, 2019 ). Going forward, whole-brain models could also be explored for explaining various tasks and learning paradigms, requiring richer node dynamics with neuromodulatory and plasticity properties (Abel et al, 2013 ; Maniglia and Seitz, 2018 ; Zhang et al, 2021 ). Finally, foundational discoveries in graph theory and non-equilibrium physics will continue to offer new insights into the mechanistic underpinnings of large-scale brain dynamics.…”

Section: Promises and Pitfallsmentioning

confidence: 99%

Whole-Brain Network Models: From Physics to Bedside

Pathak

Roy

Banerjee

2022

Front. Comput. Neurosci.

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Computational neuroscience has come a long way from its humble origins in the pioneering work of Hodgkin and Huxley. Contemporary computational models of the brain span multiple spatiotemporal scales, from single neuronal compartments to models of social cognition. Each spatial scale comes with its own unique set of promises and challenges. Here, we review models of large-scale neural communication facilitated by white matter tracts, also known as whole-brain models (WBMs). Whole-brain approaches employ inputs from neuroimaging data and insights from graph theory and non-linear systems theory to model brain-wide dynamics. Over the years, WBM models have shown promise in providing predictive insights into various facets of neuropathologies such as Alzheimer's disease, Schizophrenia, Epilepsy, Traumatic brain injury, while also offering mechanistic insights into large-scale cortical communication. First, we briefly trace the history of WBMs, leading up to the state-of-the-art. We discuss various methodological considerations for implementing a whole-brain modeling pipeline, such as choice of node dynamics, model fitting and appropriate parcellations. We then demonstrate the applicability of WBMs toward understanding various neuropathologies. We conclude by discussing ways of augmenting the biological and clinical validity of whole-brain models.

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Computational exploration of dynamic mechanisms of steady state visual evoked potentials at the whole brain level

Cited by 21 publications

References 72 publications

Segregation, integration and balance in resting‐state brain functional networks associated with bipolar disorder symptoms

Segregation, integration and balance in resting‐state brain functional networks associated with bipolar disorder symptoms

Cross-Subject Transfer Learning for Boosting Recognition Performance in SSVEP-Based BCIs

Whole-Brain Network Models: From Physics to Bedside

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