Faculty Opinions recommendation of Mind wandering away from pain dynamically engages antinociceptive and default mode brain networks.

Self-consciousness is a personality trait associated with an individual’s concern regarding observable (public) and unobservable (private) aspects of self. Prompted by previous functional magnetic resonance imaging (MRI) studies, we examined possible gray-matter expansions in emotion-related and default mode networks in individuals with higher public or private self-consciousness. One hundred healthy young adults answered the Japanese version of the Self-Consciousness Scale (SCS) questionnaire and underwent structural MRI. A voxel-based morphometry analysis revealed that individuals scoring higher on the public SCS showed expansions of gray matter in the emotion-related regions of the cingulate and insular cortices and in the default mode network of the precuneus and medial prefrontal cortex. In addition, these gray-matter expansions were particularly related to the trait of “concern about being evaluated by others”, which was one of the subfactors constituting public self-consciousness. Conversely, no relationship was observed between gray-matter volume in any brain regions and the private SCS scores. This is the first study showing that the personal trait of concern regarding public aspects of the self may cause long-term substantial structural changes in social brain networks.

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CNTM-01. Evaluating Traditional and Non-Traditional Eloquent Areas in Patients with Brain Tumors: Large-scale Network Analysis Using a Machine Learning-Based Platform

Neuro-Oncology ◽

10.1093/neuonc/noab196.899 ◽

2021 ◽

Vol 23 (Supplement_6) ◽

pp. vi224-vi224

Author(s):

Alexis Morell ◽

Daniel Eichberg ◽

Ashish Shah ◽

Evan Luther ◽

Victor Lu ◽

...

Keyword(s):

Machine Learning ◽

Brain Tumors ◽

Large Scale ◽

Diffusion Tensor ◽

Brain Networks ◽

Tumor Resection ◽

Central Executive ◽

Default Mode ◽

Eloquent Areas ◽

Neurologic Deficits

Abstract BACKGROUND Developing mapping tools that allow identification of traditional or non-traditional eloquent areas is necessary to minimize the risk of postoperative neurologic deficits. The objective of our study is to evaluate the use of a novel cloud-based platform that uses machine learning to identify cerebral networks in patients with brain tumors. METHODS We retrospectively included all adult patients who underwent surgery for brain tumor resection or thermal ablation at our Institution between the 16th of February and the 15th of May of 2021. Pre and postoperative contrast-enhanced MRI with T1-weighted and high-resolution Diffusion Tensor Imaging (DTI) sequences were uploaded into the Quicktome platform. After processing the data, we categorized the integrity of seven large-scale brain networks: sensorimotor, visual, ventral attention, central executive, default mode, dorsal attention and limbic. Affected networks were correlated with pre and postoperative clinical data, including neurologic deficits. RESULTS Thirty-five (35) patients were included in the study. The average age of the sample was 63.2 years, and 51.4% (n=18) were females. The most affected network was the central executive network (40%), followed by the dorsal attention and default mode networks (31.4%), while the least affected were the visual (11%) and ventral attention networks (17%). Patients with preoperative deficits showed a significantly higher number of altered networks before the surgery (p=0.021), compared to patients without deficits. In addition, we found that patients without neurologic deficits had an average of 2.06 large-scale networks affected, with 75% of them not being related to traditional eloquent areas as the sensorimotor, language or visual circuits. CONCLUSIONS The Quicktome platform is a practical tool that allows automatic visualization of large-scale brain networks in patients with brain tumors. Although further studies are needed, it may assist in the surgical management of traditional and non-traditional eloquent areas.

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Mind wandering and attention during focused meditation: A fine-grained temporal analysis of fluctuating cognitive states

10.31231/osf.io/4fnt6 ◽

2017 ◽

Author(s):

Wendy Hasenkamp ◽

Christine Wilson-Mendenhall ◽

Erica Duncan ◽

Lawrence Barsalou

Keyword(s):

Cognitive Processes ◽

Sustained Attention ◽

Temporal Analysis ◽

Mind Wandering ◽

Brain Regions ◽

Attentional Processing ◽

Default Mode ◽

Cognitive Correlates ◽

Cognitive States ◽

Task Positive Network

Studies have suggested that the default mode network is active during mind wandering, which is often experienced intermittently during sustained attention tasks. Conversely, an anticorrelated task-positive network is thought to subserve various forms of attentional processing. Understanding how these two systems work together is central for understanding many forms of optimal and sub-optimal task performance. Here we present a basic model of naturalistic cognitive fluctuations between mind wandering and attentional states derived from the practice of focused attention meditation. This model proposes four intervals in a cognitive cycle: mind wandering, awareness of mind wandering, shifting of attention, and sustained attention. People who train in this style of meditation cultivate their abilities to monitor cognitive processes related to attention and distraction, making them well suited to report on these mental events. Fourteen meditation practitioners performed breath-focused meditation while undergoing fMRI scanning. When participants realized their mind had wandered, they pressed a button and returned their focus to the breath. The four intervals above were then constructed around these button presses. We hypothesized that periods of mind wandering would be associated with default mode activity, whereas cognitive processes engaged during awareness of mind wandering, shifting of attention and sustained attention would engage attentional subnetworks. Analyses revealed activity in brain regions associated with the default mode during mind wandering, and in salience network regions during awareness of mind wandering. Elements of the executive network were active during shifting and sustained attention. Furthermore, activations during these cognitive phases were modulated by lifetime meditation experience. These findings support and extend theories about cognitive correlates of distributed brain networks.

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Effect of tDCS over the Right Inferior Parietal Lobule on Mind-Wandering Propensity

10.31234/osf.io/35thz ◽

2020 ◽

Author(s):

Sean Coulborn ◽

Howard Bowman ◽

Chris Miall ◽

Davinia Fernández-Espejo

Keyword(s):

Mind Wandering ◽

Inferior Parietal Lobule ◽

Double Blind ◽

Default Mode ◽

Dorsolateral Prefrontal ◽

Parietal Lobule ◽

Response Task ◽

Thought Probes ◽

The Right ◽

Subjective Reports

Mind-wandering is associated with switching our attention to internally directed thoughts and is by definition an intrinsic, self-generated cognitive function. Interestingly, previous research showed that it may be possible to modulate its propensity externally, with transcranial direct current stimulation (tDCS) targeting different regions in the default mode and executive control networks. However, these studies used highly heterogeneous montages (targeting the dorsolateral prefrontal cortex (DLPFC), the right inferior parietal lobule (IPL), or both concurrently), often showed contradicting results, and in many cases failed to replicate. Our study aimed to establish whether tDCS of the default mode network, via targeting the right IPL alone, could modulate mind-wandering propensity using a within-subjects double-blind, counterbalanced design. Participants completed a sustained attention to response task (SART) interspersed with thought-probes to capture their subjective reports of mind-wandering before and after receiving anodal, cathodal, or sham tDCS over the right IPL (with the reference over the left cheek). We found evidence for the lack of an effect of stimulation on subjective reports of mind-wandering (JZS-BF01 = 5.19), as well as on performance on the SART task (errors (JZS-BF01 = 6.79) and reaction time (JZS-BF01 = 5.94). Overall, we failed to replicate previous reports of successful modulations of mind-wandering propensity with tDCS over the IPL, instead providing evidence in support of the lack of an effect. This, and other recent unsuccessful replications call into question whether it is indeed possible to externally modulate spontaneous or self-generated cognitive processes.

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Attentional Fluctuations Influence the Neural Fidelity and Connectivity of Stimulus Representations

Journal of Cognitive Neuroscience ◽

10.1162/jocn_a_01306 ◽

2018 ◽

Vol 30 (9) ◽

pp. 1209-1228 ◽

Cited By ~ 6

Author(s):

David Rothlein ◽

Joseph DeGutis ◽

Michael Esterman

Keyword(s):

Functional Connectivity ◽

Large Scale ◽

Brain Networks ◽

Relevant Information ◽

Mental States ◽

Attention Task ◽

Default Mode ◽

Neural Representations ◽

Similarity Structure ◽

Similarity Matrices

Attention is thought to facilitate both the representation of task-relevant features and the communication of these representations across large-scale brain networks. However, attention is not “all or none,” but rather it fluctuates between stable/accurate (in-the-zone) and variable/error-prone (out-of-the-zone) states. Here we ask how different attentional states relate to the neural processing and transmission of task-relevant information. Specifically, during in-the-zone periods: (1) Do neural representations of task stimuli have greater fidelity? (2) Is there increased communication of this stimulus information across large-scale brain networks? Finally, (3) can the influence of performance-contingent reward be differentiated from zone-based fluctuations? To address these questions, we used fMRI and representational similarity analysis during a visual sustained attention task (the gradCPT). Participants ( n = 16) viewed a series of city or mountain scenes, responding to cities (90% of trials) and withholding to mountains (10%). Representational similarity matrices, reflecting the similarity structure of the city exemplars ( n = 10), were computed from visual, attentional, and default mode networks. Representational fidelity (RF) and representational connectivity (RC) were quantified as the interparticipant reliability of representational similarity matrices within (RF) and across (RC) brain networks. We found that being in the zone was characterized by increased RF in visual networks and increasing RC between visual and attentional networks. Conversely, reward only increased the RC between the attentional and default mode networks. These results diverge with analogous analyses using functional connectivity, suggesting that RC and functional connectivity in tandem better characterize how different mental states modulate the flow of information throughout the brain.

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