Mediation analysis of triple networks revealed functional feature of mindfulness from real-time fMRI neurofeedback

Kim, Hyun-Chul; Tegethoff, Marion; Meinlschmidt, Gunther; Stalujanis, Esther; Belardi, Angelo; Jo, Sung Ho; Lee, Juhyeon; Kim, Dong Youl; Yoo, Seung Schik; Lee, Jong Hwan

doi:10.1016/j.neuroimage.2019.03.066

Cited by 37 publications

(48 citation statements)

References 140 publications

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“…Unlike Scharnowski and colleagues (2015), however, we focus on the balance between two large-scale brain networks that each consist of multiple brain areas and are functionally related. Our approach is in line with two other recent neurofeedback studies that target large-scale brain network balance (Kim et al 2019;Pamplona et al, 2020). While Kim and colleagues (2019) targeted changes in functional connectivity between SN and DMN, Pamplona and colleagues (2020) took an approach similar to ours and targeted the difference in the activation between the sustained attention network and DMN.…”

Section: Discussionsupporting

confidence: 65%

Self-regulation of stress-related large-scale brain network balance using real-time fMRI Neurofeedback

Krause

Kogias

Krentz

et al. 2021

Preprint

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It has recently been shown that acute stress affects the allocation of neural resources between large-scale brain networks, and the balance between the executive control network and the salience network in particular. Maladaptation of this dynamic resource reallocation process is thought to play a major role in stress-related psychopathology, suggesting that stress resilience may be determined by the retained ability to adaptively reallocate neural resources between these two networks. Actively training this ability could hence be a potentially promising way to increase resilience in individuals at risk for developing stress-related symptomatology. Using real-time functional Magnetic Resonance Imaging, the current study investigated whether individuals can learn to self-regulate stress-related large-scale network balance. Participants were engaged in a bidirectional and implicit real-time fMRI neurofeedback paradigm in which they were intermittently provided with a visual representation of the difference signal between the average activation of the salience and executive control networks, and tasked with attempting to self-regulate this signal. Our results show that, given feedback about their performance over three training sessions, participants were able to (1) learn strategies to differentially control the balance between SN and ECN activation on demand, as well as (2) successfully transfer this newly learned skill to a situation where they (a) did not receive any feedback anymore, and (b) were exposed to an acute stressor in form of the prospect of a mild electric stimulation. The current study hence constitutes an important first successful demonstration of neurofeedback training based on stress-related large-scale network balance - a novel approach that has the potential to train control over the central response to stressors in real-life and could build the foundation for future clinical interventions that aim at increasing resilience.

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Section: Discussionsupporting

confidence: 65%

Self-regulation of stress-related large-scale brain network balance using real-time fMRI Neurofeedback

Krause

Kogias

Krentz

et al. 2021

Preprint

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“…Extracting effective features from FBNs is a critical step to improve classification performance and interpretability of brain functional networks (Kim et al, 2019 ; Qiu et al, 2019 ). As shown in Figure 1 (1), three kinds of feature representations have been employed for FBN-based disease identification based on different granularities, including global-level topology features, node-level features, and edge-level features.…”

Section: Introductionmentioning

confidence: 99%

Modularity-Guided Functional Brain Network Analysis for Early-Stage Dementia Identification

et al. 2021

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Function brain network (FBN) analysis has shown great potential in identifying brain diseases, such as Alzheimer's disease (AD) and its prodromal stage, namely mild cognitive impairment (MCI). It is essential to identify discriminative and interpretable features from function brain networks, so as to improve classification performance and help us understand the pathological mechanism of AD-related brain disorders. Previous studies usually extract node statistics or edge weights from FBNs to represent each subject. However, these methods generally ignore the topological structure (such as modularity) of FBNs. To address this issue, we propose a modular-LASSO feature selection (MLFS) framework that can explicitly model the modularity information to identify discriminative and interpretable features from FBNs for automated AD/MCI classification. Specifically, the proposed MLFS method first searches the modular structure of FBNs through a signed spectral clustering algorithm, and then selects discriminative features via a modularity-induced group LASSO method, followed by a support vector machine (SVM) for classification. To evaluate the effectiveness of the proposed method, extensive experiments are performed on 563 resting-state functional MRI scans from the public ADNI database to identify subjects with AD/MCI from normal controls and predict the future progress of MCI subjects. Experimental results demonstrate that our method is superior to previous methods in both tasks of AD/MCI identification and MCI conversion prediction, and also helps discover discriminative brain regions and functional connectivities associated with AD.

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“…These three core brain networks, namely CEN, SN, and DMN, have been identified by functional connectivity (FC) analyses predominantly at resting state fMRI, while subjects lie in the scanner and are not asked to engage in any particular task. Since the ascending inputs from the visceral organs continuously reach numerous cortical and subcortical regions, many researchers claim that the visceral signals are the continuous internal stimuli that contribute and shape the spontaneous brain activity and intrinsic brain-networks (Azzalini et al, 2019;Kim et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

Heart Rate Variability as an Index of Differential Brain Dynamics at Rest and After Acute Stress Induction

Chand

Jamalabadi

et al. 2020

Front. Neurosci.

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The brain continuously receives input from the internal and external environment. Using this information, the brain exerts its influence on both itself and the body to facilitate an appropriate response. The dynamic interplay between the brain and the heart and how external conditions modulate this relationship deserves attention. In high-stress situations, synchrony between various brain regions such as the prefrontal cortex and the heart may alter. This flexibility is believed to facilitate transitions between functional states related to cognitive, emotional, and especially autonomic activity. This study examined the dynamic temporal functional association of heart rate variability (HRV) with the interaction between three main canonical brain networks in 38 healthy male subjects at rest and directly after a psychosocial stress task. A sliding window approach was used to estimate the functional connectivity (FC) among the salience network (SN), central executive network (CEN), and default mode network (DMN) in 60-s windows on time series of blood-oxygen-level dependent (BOLD) signal. FC between brain networks was calculated by Pearson correlation. A multilevel linear mixed model was conducted to examine the window-by-window association between the root mean square of successive differences between normal heartbeats (RMSSD) and FC of network-pairs across sessions. Our findings showed that the minute-by-minute correlation between the FC and RMSSD was significantly stronger between DMN and CEN than for SN and CEN in the baseline session [b = 4.36, t(5025) = 3.20, p = 0.006]. Additionally, this differential relationship between network pairs and RMSSD disappeared after the stress task; FC between DMN and CEN showed a weaker correlation with RMSSD in comparison to baseline [b = −3.35, t(5025) = −3.47, p = 0.006]. These results suggest a dynamic functional interplay between HRV and the functional association between brain networks that varies depending on the needs created by changing conditions.

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Mediation analysis of triple networks revealed functional feature of mindfulness from real-time fMRI neurofeedback

Cited by 37 publications

References 140 publications

Self-regulation of stress-related large-scale brain network balance using real-time fMRI Neurofeedback

Self-regulation of stress-related large-scale brain network balance using real-time fMRI Neurofeedback

Modularity-Guided Functional Brain Network Analysis for Early-Stage Dementia Identification

Heart Rate Variability as an Index of Differential Brain Dynamics at Rest and After Acute Stress Induction

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