States of Mind: Characterizing the Neural Bases of Focus and Mind-wandering through Dynamic Functional Connectivity

2017 ◽  
Vol 29 (3) ◽  
pp. 495-506 ◽  
Author(s):  
Benjamin W. Mooneyham ◽  
Michael D. Mrazek ◽  
Alissa J. Mrazek ◽  
Kaita L. Mrazek ◽  
Dawa T. Phillips ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Mind Wandering ◽  
Mental States ◽  
Brain Regions ◽  
Focused Attention ◽  
Supporting Evidence ◽  
Dispositional Mindfulness ◽  
Dynamic Functional Connectivity ◽  
Brain States ◽  
The Mind

During tasks that require continuous engagement, the mind alternates between mental states of focused attention and mind-wandering. Existing research has assessed the functional connectivity of intrinsic brain networks underlying the experience and training of these mental states using “static” approaches that assess connectivity across an entire task. To disentangle the different functional connectivity between brain regions that occur as the mind fluctuates between discrete brain states, we employed a dynamic functional connectivity approach that characterized brain activity using a sliding window. This approach identified distinct states of functional connectivity between regions of the executive control, salience, and default networks during a task requiring sustained attention to the sensations of breathing. The frequency of these distinct brain states demonstrated opposing correlations with dispositional mindfulness, suggesting a correspondence to the mental states of focused attention and mind-wandering. We then determined that an intervention emphasizing the cultivation of mindfulness increased the frequency of the state that had been associated with a greater propensity for focused attention, especially for those who improved most in dispositional mindfulness. These findings provide supporting evidence that mind-wandering involves the corecruitment of brain regions within the executive and default networks. More generally, this work illustrates how emerging neuroimaging methods may allow for the characterization of discrete brain states based on patterns of functional connectivity even when external indications of these states are difficult or impossible to measure.

scholarly journals Activity in the brain’s valuation and mentalizing networks is associated with propagation of online recommendations

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Elisa C. Baek ◽  
Matthew Brook O’Donnell ◽  
Christin Scholz ◽  
Rui Pei ◽  
Javier O. Garcia ◽  
...  
Keyword(s):  
Social Media ◽  
Word Of Mouth ◽  
Mental States ◽  
Brain Regions ◽  
Prior Work ◽  
Opinion Change ◽  
Wide Range ◽  
The Mind ◽  
Mentalizing System

AbstractWord of mouth recommendations influence a wide range of choices and behaviors. What takes place in the mind of recommendation receivers that determines whether they will be successfully influenced? Prior work suggests that brain systems implicated in assessing the value of stimuli (i.e., subjective valuation) and understanding others’ mental states (i.e., mentalizing) play key roles. The current study used neuroimaging and natural language classifiers to extend these findings in a naturalistic context and tested the extent to which the two systems work together or independently in responding to social influence. First, we show that in response to text-based social media recommendations, activity in both the brain’s valuation system and mentalizing system was associated with greater likelihood of opinion change. Second, participants were more likely to update their opinions in response to negative, compared to positive, recommendations, with activity in the mentalizing system scaling with the negativity of the recommendations. Third, decreased functional connectivity between valuation and mentalizing systems was associated with opinion change. Results highlight the role of brain regions involved in mentalizing and positive valuation in recommendation propagation, and further show that mentalizing may be particularly key in processing negative recommendations, whereas the valuation system is relevant in evaluating both positive and negative recommendations.

scholarly journals A Novel Unit-Based Personalized Fingerprint Feature Selection Strategy for Dynamic Functional Connectivity Networks

2021 ◽  
Vol 15 ◽  
Author(s):  
Feng Zhao ◽  
Zhiyuan Chen ◽  
Islem Rekik ◽  
Peiqiang Liu ◽  
Ning Mao ◽  
...  
Keyword(s):  
Feature Selection ◽  
Functional Connectivity ◽  
Evaluation Method ◽  
Brain Regions ◽  
Autism Spectrum ◽  
Superior Performance ◽  
Selection Strategy ◽  
Dynamic Functional Connectivity ◽  
Discriminative Feature ◽  
Fingerprint Feature

The sliding-window-based dynamic functional connectivity networks (SW-D-FCN) derive from resting-state functional Magnetic Resonance Imaging has become an increasingly useful tool in the diagnosis of various neurodegenerative diseases. However, it is still challenging to learn how to extract and select the most discriminative features from SW-D-FCN. Conventionally, existing methods opt to select a single discriminative feature set or concatenate a few more from the SW-D-FCN. However, such reductionist strategies may fail to fully capture the personalized discriminative characteristics contained in each functional connectivity (FC) sequence of the SW-D-FCN. To address this issue, we propose a unit-based personalized fingerprint feature selection (UPFFS) strategy to better capture the most discriminative feature associated with a target disease for each unit. Specifically, we regard the FC sequence between any pair of brain regions of interest (ROIs) is regarded as a unit. For each unit, the most discriminative feature is identified by a specific feature evaluation method and all the most discriminative features are then concatenated together as a feature set for the subsequent classification task. In such a way, the personalized fingerprint feature derived from each FC sequence can be fully mined and utilized in classification decision. To illustrate the effectiveness of the proposed strategy, we conduct experiments to distinguish subjects diagnosed with autism spectrum disorder from normal controls. Experimental results show that the proposed strategy can select relevant discriminative features and achieve superior performance to benchmark methods.

scholarly journals Beyond linear dynamic functional connectivity: a vine copula change point model

2021 ◽  
Author(s):  
Xin Xiong ◽  
Ivor Cribben
Keyword(s):  
Functional Connectivity ◽  
Change Point ◽  
Sliding Window ◽  
Brain Regions ◽  
Change Points ◽  
Fmri Data ◽  
Data Sets ◽  
Dynamic Functional Connectivity ◽  
Vine Copula ◽  
Vine Copulas

To estimate dynamic functional connectivity for functional magnetic resonance imaging (fMRI) data, two approaches have dominated: sliding window and change point methods. While computationally feasible, the sliding window approach has several limitations. In addition, the existing change point methods assume a Gaussian distribution for and linear dependencies between the fMRI time series. In this work, we introduce a new methodology called Vine Copula Change Point (VCCP) to estimate change points in the functional connectivity network structure between brain regions. It uses vine copulas, various state-of-the-art segmentation methods to identify multiple change points, and a likelihood ratio test or the stationary bootstrap for inference. The vine copulas allow for various forms of dependence between brain regions including tail, symmetric and asymmetric dependence, which has not been explored before in the analysis of neuroimaging data. We apply VCCP to various simulation data sets and to two fMRI data sets: a reading task and an anxiety inducing experiment. In particular, for the former data set, we illustrate the complexity of textual changes during the reading of Chapter 9 in Harry Potter and the Sorcerer's Stone and find that change points across subjects are related to changes in more than one type of textual attributes. Further, the graphs created by the vine copulas indicate the importance of working beyond Gaussianity and linear dependence. Finally, the R package vccp implementing the methodology from the paper is available from CRAN.

scholarly journals Psilocybin modulation of dynamic functional connectivity is associated with plasma psilocin and subjective effects

2021 ◽  
Author(s):  
Anders S Olsen ◽  
Anders Lykkebo-Valloee ◽  
Brice Ozenne ◽  
Martin K Madsen ◽  
Dea Siggaard Stenbaek ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Resting State ◽  
Dwell Time ◽  
Brain State ◽  
Healthy Individuals ◽  
Acute Effects ◽  
Dynamic Functional Connectivity ◽  
Negatively Associated ◽  
State Dynamic ◽  
Brain States

Background: Psilocin, the neuroactive metabolite of psilocybin, is a serotonergic psychedelic that induces an acute altered state of consciousness, evokes lasting changes in mood and personality in healthy individuals, and has potential as an antidepressant treatment. Examining the acute effects of psilocin on resting-state dynamic functional connectivity implicates network-level connectivity motifs that may underlie acute and lasting behavioral and clinical effects. Aim: Evaluate the association between resting-state dynamic functional connectivity (dFC) characteristics and plasma psilocin level (PPL) and subjective drug intensity (SDI) before and right after intake of a psychedelic dose of psilocybin in healthy humans. Methods: Fifteen healthy individuals completed the study. Before and at multiple time points after psilocybin intake, we acquired 10-minute resting-state blood-oxygen-level-dependent functional magnetic resonance imaging scans. Leading Eigenvector Dynamics Analysis (LEiDA) and diametrical clustering were applied to estimate discrete, sequentially active brain states. We evaluated associations between the fractional occurrence of brain states during a scan session and PPL and SDI using linear mixed-effects models. We examined associations between brain state dwell time and PPL and SDI using frailty Cox proportional hazards survival analysis. Results: Fractional occurrences for two brain states characterized by lateral frontoparietal and medial fronto-parietal-cingulate coherence were statistically significantly negatively associated with PPL and SDI. Dwell time for these brain states was negatively associated with SDI and, to a lesser extent, PPL. Conversely, fractional occurrence and dwell time of a fully connected brain state was positively associated with PPL and SDI. Conclusion: Our findings suggest that the acute perceptual psychedelic effects induced by psilocybin may stem from drug-level associated decreases in the occurrence and duration of lateral and medial frontoparietal connectivity motifs in exchange for increases in a uniform connectivity structure. We apply and argue for a modified approach to modeling eigenvectors produced by LEiDA that more fully acknowledges their underlying structure. Together these findings contribute to a more comprehensive neurobiological framework underlying acute effects of serotonergic psychedelics.

scholarly journals Nuisance Effects and the Limitations of Nuisance Regression in Dynamic Functional Connectivity fMRI

2018 ◽  
Author(s):  
Alican Nalci ◽  
Bhaskar D. Rao ◽  
Thomas T. Liu
Keyword(s):  
Functional Connectivity ◽  
Resting State Fmri ◽  
Brain Regions ◽  
Blood Oxygenation ◽  
Dynamic Functional Connectivity ◽  
Temporal Fluctuations ◽  
Before And After ◽  
The Difference ◽  
Time Courses ◽  
The Relationship

AbstractIn resting-state fMRI, dynamic functional connectivity (DFC) measures are used to characterize temporal changes in the brain’s intrinsic functional connectivity. A widely used approach for DFC estimation is the computation of the sliding window correlation between blood oxygenation level dependent (BOLD) signals from different brain regions. Although the source of temporal fluctuations in DFC estimates remains largely unknown, there is growing evidence that they may reflect dynamic shifts between functional brain networks. At the same time, recent findings suggest that DFC estimates might be prone to the influence of nuisance factors such as the physiological modulation of the BOLD signal. Therefore, nuisance regression is used in many DFC studies to regress out the effects of nuisance terms prior to the computation of DFC estimates. In this work we examined the relationship between DFC estimates and nuisance factors. We found that DFC estimates were significantly correlated with temporal fluctuations in the magnitude (norm) of various nuisance regressors, with significant correlations observed in the majority (76%) of the cases examined. Significant correlations between the DFC estimates and nuisance regressor norms were found even when the underlying correlations between the nuisance and fMRI time courses were relatively small. We then show that nuisance regression does not eliminate the relationship between DFC estimates and nuisance norms, with significant correlations observed in the majority (71%) of the cases examined after nuisance regression. We present theoretical bounds on the difference between DFC estimates obtained before and after nuisance regression and relate these bounds to limitations in the efficacy of nuisance regression with regards to DFC estimates.

scholarly journals Multifractal and entropy analysis of resting-state electroencephalography reveals spatial organization in local dynamic functional connectivity

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Frigyes Samuel Racz ◽  
Orestis Stylianou ◽  
Peter Mukli ◽  
Andras Eke
Keyword(s):  
Functional Connectivity ◽  
Resting State ◽  
Spatial Organization ◽  
Brain Activity ◽  
Surrogate Data ◽  
Brain Regions ◽  
Local Dynamic ◽  
Dynamic Functional Connectivity ◽  
Multifractal Scaling ◽  
The Brain

Abstract Functional connectivity of the brain fluctuates even in resting-state condition. It has been reported recently that fluctuations of global functional network topology and those of individual connections between brain regions expressed multifractal scaling. To expand on these findings, in this study we investigated if multifractality was indeed an inherent property of dynamic functional connectivity (DFC) on the regional level as well. Furthermore, we explored if local DFC showed region-specific differences in its multifractal and entropy-related features. DFC analyses were performed on 62-channel, resting-state electroencephalography recordings of twelve young, healthy subjects. Surrogate data testing verified the true multifractal nature of regional DFC that could be attributed to the presumed nonlinear nature of the underlying processes. Moreover, we found a characteristic spatial distribution of local connectivity dynamics, in that frontal and occipital regions showed stronger long-range correlation and higher degree of multifractality, whereas the highest values of entropy were found over the central and temporal regions. The revealed topology reflected well the underlying resting-state network organization of the brain. The presented results and the proposed analysis framework could improve our understanding on how resting-state brain activity is spatio-temporally organized and may provide potential biomarkers for future clinical research.

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