Sustained activity within the default mode network during an implicit memory task

Yang, Jun; Weng, Xuchu; Zang, Yu‐Feng; Xu, Mingfang; Xu, Xiaohong

doi:10.1016/j.cortex.2009.05.002

Cited by 31 publications

(22 citation statements)

References 59 publications

(142 reference statements)

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“…We did not observe any task-related deactivation in the time course of this component, nor did its power depend upon the presence of a task. This is consistent with a recent report that implicit memory tasks do not disrupt default mode network activity (Yang et al, 2010). Yet, this behavior strongly differed from that of component II, which we consider to be more characteristic for the default mode network.…”

Section: [B] a Non-causal Effective Connectivity Model Was Evaluated supporting

confidence: 93%

Robustness of intrinsic connectivity networks in the human brain to the presence of acoustic scanner noise

Langers

Dijk²

2011

NeuroImage

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Evoked responses in functional magnetic resonance imaging (fMRI) are affected by the presence of acoustic scanner noise (ASN). Particularly, stimulus-related activation of the auditory system and deactivation of the default mode network have repeatedly been shown to diminish. In contrast, little is known about the influence of ASN on the spontaneous fluctuations in brain activity that are crucial for network-related neuroimaging methods like independent component analysis (ICA) or functional and effective connectivity analysis (ECA). The present study assessed the robustness of intrinsic connectivity networks in the human brain to the presence of ASN by comparing 'silent' (sparse) and 'noisy' (continuous) acquisition schemes, both during task performance and during rest. In agreement with existing literature, ASN strongly diminished conventional evoked response levels. In contrast, ICA and ECA robustly identified similar functional networks regardless of the scanning method. ASN affected the strength of only few independent components, and effective connectivity was hardly sensitive to ASN overall. However, unexpectedly, ICA revealed notable differences in the underlying neurodynamics. In particular, low-frequency network oscillations dominated in the commonly used continuous scanning environment, but signal spectra were significantly flatter during the less noisy sparse scanning runs. We tentatively attribute these differences to the ubiquitous influence of ASN on alertness and arousal.

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Section: [B] a Non-causal Effective Connectivity Model Was Evaluated supporting

confidence: 93%

Robustness of intrinsic connectivity networks in the human brain to the presence of acoustic scanner noise

Langers

Dijk²

2011

NeuroImage

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“…The angular gyrus is in general recognized as an associative cortex for semantic processes (Seghier et al, 2011), emotional stimuli (Kohn et al, 2011), episodic memory (Mazoyer et al, 2001; Anticevic et al, 2010; Yang et al, 2010), mental arithmetic (Grabner et al, 2009) and self-relevant internal cognitive processes (Andrews-Hanna et al, 2010). These rather different tasks all share the generation of an internal representation or image crucial for the respective performance, thus the AG’s association with social cognition may be explained by generation of imagined or remembered situations in social cognitive tasks.…”

Section: Discussionmentioning

confidence: 99%

Neural network of cognitive emotion regulation — An ALE meta-analysis and MACM analysis

et al. 2014

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Cognitive regulation of emotions is a fundamental prerequisite for intact social functioning which impacts on both well being and psychopathology. The neural underpinnings of this process have been studied intensively in recent years, without, however, a general consensus. We here quantitatively summarize the published literature on cognitive emotion regulation using activation likelihood estimation in fMRI and PET (23 studies/479 subjects). In addition, we assessed the particular functional contribution of identified regions and their interactions using quantitative functional inference and meta-analytic connectivity modeling, respectively. In doing so, we developed a model for the core brain network involved in emotion regulation of emotional reactivity. According to this, the superior temporal gyrus, angular gyrus and (pre) supplementary motor area should be involved in execution of regulation initiated by frontal areas. The dorsolateral prefrontal cortex may be related to regulation of cognitive processes such as attention, while the ventrolateral prefrontal cortex may not necessarily reflect the regulatory process per se, but signals salience and therefore the need to regulate. We also identified a cluster in the anterior middle cingulate cortex as a region, which is anatomically and functionally in an ideal position to influence behavior and subcortical structures related to affect generation. Hence this area may play a central, integrative role in emotion regulation. By focusing on regions commonly active across multiple studies, this proposed model should provide important a priori information for the assessment of dysregulated emotion regulation in psychiatric disorders.

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“…Standing alone, this conclusion is based on reverse inference (Poldrack, 2006) and needs to be qualified because the aINS is also active in a variety of tasks not explicitly related to emotions (Yang et al, 2010). Still, additional evidence comes from behavioral research on the influence of emotions on decision making.…”

Section: Discussionmentioning

confidence: 99%

Neural Processing of Risk

Mohr¹,

Biele²,

Heekeren³

2010

J. Neurosci.

379

302

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In our everyday life, we often have to make decisions with risky consequences, such as choosing a restaurant for dinner or choosing a form of retirement saving. To date, however, little is known about how the brain processes risk. Recent conceptualizations of risky decision making highlight that it is generally associated with emotions but do not specify how emotions are implicated in risk processing. Moreover, little is known about risk processing in non-choice situations and how potential losses influence risk processing. Here we used quantitative meta-analyses of functional magnetic resonance imaging experiments on risk processing in the brain to investigate (1) how risk processing is influenced by emotions, (2) how it differs between choice and non-choice situations, and (3) how it changes when losses are possible. By showing that, over a range of experiments and paradigms, risk is consistently represented in the anterior insula, a brain region known to process aversive emotions such as anxiety, disappointment, or regret, we provide evidence that risk processing is influenced by emotions. Furthermore, our results show risk-related activity in the dorsolateral prefrontal cortex and the parietal cortex in choice situations but not in situations in which no choice is involved or a choice has already been made. The anterior insula was predominantly active in the presence of potential losses, indicating that potential losses modulate risk processing.

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Sustained activity within the default mode network during an implicit memory task

Cited by 31 publications

References 59 publications

Robustness of intrinsic connectivity networks in the human brain to the presence of acoustic scanner noise

Robustness of intrinsic connectivity networks in the human brain to the presence of acoustic scanner noise

Neural network of cognitive emotion regulation — An ALE meta-analysis and MACM analysis

Neural Processing of Risk

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