The Temporal Dynamics of Spontaneous Emotional Brain States and Their Implications for Mental Health

2021 ◽  
pp. 1-14
Author(s):  
Philip A. Kragel ◽  
Ahmad R. Hariri ◽  
Kevin S. LaBar
Keyword(s):  
Mental Health ◽  
Temporal Dynamics ◽  
Brain Activity ◽  
Resting State Fmri ◽  
State Transitions ◽  
Brain State ◽  
Emotional States ◽  
Affective States ◽  
Emotional Responding ◽  
Brain States

Abstract Temporal processes play an important role in elaborating and regulating emotional responding during routine mind wandering. However, it is unknown whether the human brain reliably transitions among multiple emotional states at rest and how psychopathology alters these affect dynamics. Here, we combined pattern classification and stochastic process modeling to investigate the chronometry of spontaneous brain activity indicative of six emotions (anger, contentment, fear, happiness, sadness, and surprise) and a neutral state. We modeled the dynamic emergence of these brain states during resting-state fMRI and validated the results across two population cohorts—the Duke Neurogenetics Study and the Nathan Kline Institute Rockland Sample. Our findings indicate that intrinsic emotional brain dynamics are effectively characterized as a discrete-time Markov process, with affective states organized around a neutral hub. The centrality of this network hub is disrupted in individuals with psychopathology, whose brain state transitions exhibit greater inertia and less frequent resetting from emotional to neutral states. These results yield novel insights into how the brain signals spontaneous emotions and how alterations in their temporal dynamics contribute to compromised mental health.

scholarly journals Movie viewing elicits rich and reliable brain state dynamics

2020 ◽  
Vol 11 (1) ◽  
Author(s):  
Johan N. van der Meer ◽  
Michael Breakspear ◽  
Luke J. Chang ◽  
Saurabh Sonkusare ◽  
Luca Cocchi
Keyword(s):  
Resting State ◽  
Brain Function ◽  
Temporal Dynamics ◽  
Brain Activity ◽  
State Transitions ◽  
Brain State ◽  
The Social ◽  
Brain States ◽  
Functional States ◽  
Intrinsic Brain Activity

Abstract Adaptive brain function requires that sensory impressions of the social and natural milieu are dynamically incorporated into intrinsic brain activity. While dynamic switches between brain states have been well characterised in resting state acquisitions, the remodelling of these state transitions by engagement in naturalistic stimuli remains poorly understood. Here, we show that the temporal dynamics of brain states, as measured in fMRI, are reshaped from predominantly bistable transitions between two relatively indistinct states at rest, toward a sequence of well-defined functional states during movie viewing whose transitions are temporally aligned to specific features of the movie. The expression of these brain states covaries with different physiological states and reflects subjectively rated engagement in the movie. In sum, a data-driven decoding of brain states reveals the distinct reshaping of functional network expression and reliable state transitions that accompany the switch from resting state to perceptual immersion in an ecologically valid sensory experience.

scholarly journals LSD flattens the brain′s energy landscape: evidence from receptor-informed network control theory

2021 ◽  
Author(s):  
S. Parker Singleton ◽  
Andrea I Luppi ◽  
Robin L. Carhart-Harris ◽  
Josephine Cruzat ◽  
Leor Roseman ◽  
...  
Keyword(s):  
Control Theory ◽  
Subjective Experience ◽  
Brain Activity ◽  
Energy Landscape ◽  
Network Control ◽  
State Transitions ◽  
Brain State ◽  
Control Analysis ◽  
Brain States ◽  
The Brain

Psychedelics like lysergic acid diethylamide (LSD) offer a powerful window into the function of the human brain and mind, by temporarily altering subjective experience through their neurochemical effects. The RElaxed Beliefs Under Psychedelics (REBUS) model postulates that 5-HT2a receptor agonism allows the brain to explore its dynamic landscape more readily, as suggested by more diverse (entropic) brain activity. Formally, this effect is theorized to correspond to a reduction in the energy required to transition between different brain-states, i.e. a ″flattening of the energy landscape.″ However, this hypothesis remains thus far untested. Here, we leverage network control theory to map the brain′s energy landscape, by quantifying the energy required to transition between recurrent brain states. In accordance with the REBUS model, we show that LSD reduces the energy required for brain-state transitions, and, furthermore, that this reduction in energy correlates with more frequent state transitions and increased entropy of brain-state dynamics. Through network control analysis that incorporates the spatial distribution of 5-HT2a receptors, we demonstrate the specific role of this receptor in flattening the brain′s energy landscape. Also, in accordance with REBUS, we show that the occupancy of bottom-up states is increased by LSD. In addition to validating fundamental predictions of the REBUS model of psychedelic action, this work highlights the potential of receptor-informed network control theory to provide mechanistic insights into pharmacological modulation of brain dynamics.

scholarly journals Age-dependent electroencephalogram (EEG) patterns during sevoflurane general anesthesia in infants

eLife ◽  
2015 ◽  
Vol 4 ◽  
Author(s):  
Laura Cornelissen ◽  
Seong-Eun Kim ◽  
Patrick L Purdon ◽  
Emery N Brown ◽  
Charles B Berde
Keyword(s):  
General Anesthesia ◽  
Temporal Dynamics ◽  
Brain Activity ◽  
Body Movement ◽  
Video Recording ◽  
Brain State ◽  
Alpha Power ◽  
State Monitoring ◽  
Spatio Temporal ◽  
Electroencephalogram Eeg

Electroencephalogram (EEG) approaches may provide important information about developmental changes in brain-state dynamics during general anesthesia. We used multi-electrode EEG, analyzed with multitaper spectral methods and video recording of body movement to characterize the spatio-temporal dynamics of brain activity in 36 infants 0–6 months old when awake, and during maintenance of and emergence from sevoflurane general anesthesia. During maintenance: (1) slow-delta oscillations were present in all ages; (2) theta and alpha oscillations emerged around 4 months; (3) unlike adults, all infants lacked frontal alpha predominance and coherence. Alpha power was greatest during maintenance, compared to awake and emergence in infants at 4–6 months. During emergence, theta and alpha power decreased with decreasing sevoflurane concentration in infants at 4–6 months. These EEG dynamic differences are likely due to developmental factors including regional differences in synaptogenesis, glucose metabolism, and myelination across the cortex. We demonstrate the need to apply age-adjusted analytic approaches to develop neurophysiologic-based strategies for pediatric anesthetic state monitoring.

scholarly journals Concordance Among Indices of Intrinsic Brain Function: Insights from Inter-Individual Variation and Temporal Dynamics

2016 ◽  
Author(s):  
Chao-Gan Yan ◽  
Zhen Yang ◽  
Stanley J. Colcombe ◽  
Xi-Nian Zuo ◽  
Michael P. Milham
Keyword(s):  
Individual Variation ◽  
Brain Function ◽  
Temporal Dynamics ◽  
Brain Activity ◽  
Low Frequency ◽  
Resting State Fmri ◽  
Surrogate Data ◽  
Linear Increase ◽  
Intrinsic Brain Activity ◽  
High Degree

ABSTRACTVarious resting-state fMRI (R-fMRI) measures have been developed to characterize intrinsic brain activity. While each of these measures has gained a growing presence in the literature, questions remain regarding the common and unique aspects these indices capture. The present work provided a comprehensive examination of inter-individual variation and intra-individual temporal variation for commonly used measures, including fractional amplitude of low frequency fluctuations, regional homogeneity, voxel-mirrored homotopic connectivity, network centrality and global signal correlation. Regardless of whether examining intra-individual or inter-individual variation, we found that these definitionally distinct R-fMRI indices tend to exhibit a relatively high degree of covariation, which doesn’t exist in phase randomized surrogate data. As a measure of intrinsic brain function, concordance for R-fMRI indices was negatively correlated with age across individuals (i.e., concordance among functional indices decreased with age). To understand the functional significance of concordance, we noted that higher concordance was generally associated with higher strengths of R-fMRI indices, regardless of whether looking through the lens of inter-individual (i.e., high vs. low concordance participants) or intra-individual (i.e., high vs. low concordance states identified via temporal dynamic analyses) differences. We also noted a linear increase in functional concordance together with the R-fMRI indices through the scan, which may suggest a decrease in arousal. The current study demonstrated an enriched picture regarding the relationship among the R-fMRI indices, as well as provided new insights in examining dynamic states within and between individuals.

scholarly journals Characterizing Emotional State Transitions During Prolonged Use of a Mindfulness and Meditation App: An Observational Study (Preprint)

2020 ◽  
Author(s):  
Argus Athanas ◽  
Jamison McCorrison ◽  
Julie Campiston ◽  
Nick Bender ◽  
Jamie Price ◽  
...  
Keyword(s):  
Mental Health ◽  
Health Care ◽  
Mental Health Care ◽  
Access To Health Care ◽  
Emotional State ◽  
Favorable Effect ◽  
State Transitions ◽  
Emotional States ◽  
Digital Device ◽  
Mixed Effect

BACKGROUND The increasing demand for mental health care, shortages in mental healthcare providers, and unequal access to health care generally has created a need for innovative approaches to mental health care. Digital device apps – including ‘digital therapeutics’ – that provide recommendations and feedback for dealing with stress, depression, and other mental health issues, can be used to adjust mood and show promise for helping meet this demand. In addition, the recommendations delivered through such apps can also be tailored to an individual’s needs (i.e., personalized) and thereby potentially provide greater benefits than traditional ‘one-size-fits-all’ recommendations. OBJECTIVE We sought to characterize individual transitions from one emotional state to another during the prolonged use of a digital app designed to provide a user with guided meditations based on their initial, potentially negative, emotional state. Understanding the factors that mediate such transitions can lead to improved recommendations for specific mindfulness and meditation interventions or activities (MMAs) provided in a mental health app. METHODS We analyzed data collected during the use of the Stop, Breathe and Think (SBT) mindfulness app. The SBT app prompts users to input their emotional state prior to, and immediately after, engaging with MMAs recommended by the app. Data were collected on more than 650,000 SBT users engaging in nearly 5 million MMAs. We limited the scope of our analysis to users with 10 or more MMA sessions that included at least 6 basal emotional state evaluations. Using clustering techniques, we grouped emotions recorded by individual users and then applied longitudinal mixed effect models to assess the effects that individual recommended MMAs had on transitions from one group of emotions to another. RESULTS We found that basal emotional states have a strong influence on transitions from one emotional state to another after MMA engagements. We also found that different MMAs impact these transitions, and many were effective in eliciting a healthy transition but only under certain conditions. In addition, we also observed gender and age effects on these transitions. CONCLUSIONS We find that the initial emotional state of an SBT app user has an impact on which SBT MMAs will have a favorable effect on their transition from one emotional state to another. Our results have implications for the design and use of guided mental health recommendations for digital device apps. CLINICALTRIAL

scholarly journals A generalizable multivariate brain pattern for interpersonal guilt

2019 ◽  
Author(s):  
Hongbo Yu ◽  
Leonie Koban ◽  
Luke J. Chang ◽  
Ullrich Wagner ◽  
Anjali Krishnan ◽  
...  
Keyword(s):  
Brain Activity ◽  
Test Sample ◽  
Training Sample ◽  
Brain State ◽  
Emotional States ◽  
Social Emotional ◽  
Social Emotions ◽  
Interpersonal Guilt ◽  
Experienced Pain ◽  
Independent Test Sample

AbstractFeeling guilty when we have wronged another is a crucial aspect of prosociality, but its neurobiological bases are elusive. Although multivariate patterns of brain activity show promise for developing brain measures linked to specific emotions, it is less clear whether brain activity can be trained to detect more complex social emotional states such as guilt. Here, we identified a distributed Guilt-Related Brain Signature (GRBS) across two independent neuroimaging datasets that used interpersonal interactions to evoke guilt. This signature discriminated conditions associated with interpersonal guilt from closely matched control conditions in a cross-validated training sample (N = 24; Chinese population) and in an independent test sample (N = 19; Swiss population). However, it did not respond to observed or experienced pain, or recalled guilt. Moreover, the GRBS only exhibited weak spatial similarity with other brain signatures of social affective processes, further indicating the specificity of the brain state it represents. These findings provide a step towards developing biological markers of social emotions, which could serve as important tools to investigate guilt-related brain processes in both healthy and clinical populations.

scholarly journals EEG microstate dynamics indicate a U-shaped path to propofol-induced loss of consciousness

2021 ◽  
Author(s):  
Fiorenzo Artoni ◽  
Julien Maillard ◽  
Juliane Britz ◽  
Martin Seeber ◽  
Christopher Lysakowski ◽  
...  
Keyword(s):  
Temporal Dynamics ◽  
Brain Activity ◽  
Altered States Of Consciousness ◽  
Initial Increase ◽  
Altered States ◽  
Stream Of Consciousness ◽  
Sequence Complexity ◽  
States Of Consciousness ◽  
Brain States ◽  
The Brain

It is commonly believed that the stream of consciousness is not continuous but parsed into transient brain states manifesting themselves as discrete spatiotemporal patterns of global neuronal activity. Electroencephalographical (EEG) microstates are proposed as the neurophysiological correlates of these transiently stable brain states that last for fractions of seconds. To further understand the link between EEG microstate dynamics and consciousness, we continuously recorded high-density EEG in 23 surgical patients from their awake state to unconsciousness, induced by step-wise increasing concentrations of the intravenous anesthetic propofol. Besides the conventional parameters of microstate dynamics, we introduce a new method that estimates the complexity of microstate sequences. The brain activity under the surgical anesthesia showed a decreased sequence complexity of the stereotypical microstates, which became sparser and longer-lasting. However, we observed an initial increase in microstates' temporal dynamics and complexity with increasing depth of sedation leading to a distinctive U-shape that may be linked to the paradoxical excitation induced by moderate levels of propofol. Our results support the idea that the brain is in a metastable state under normal conditions, balancing between order and chaos in order to flexibly switch from one state to another. The temporal dynamics of EEG microstates indicate changes of this critical balance between stability and transition that lead to altered states of consciousness.

scholarly journals Macroscopic quantities of collective brain activity during wakefulness and anesthesia

2021 ◽  
Author(s):  
Adrián Ponce-Alvarez ◽  
Lynn Uhrig ◽  
Nikolas Deco ◽  
Camilo M. Signorelli ◽  
Morten L. Kringelbach ◽  
...  
Keyword(s):  
Brain Activity ◽  
Brain Regions ◽  
Brain State ◽  
Loss Of Consciousness ◽  
Information Theoretic ◽  
Maximum Entropy Models ◽  
Information Theoretic Measures ◽  
Brain States ◽  
Parietal Cortices ◽  
The Brain

AbstractThe study of states of arousal is key to understand the principles of consciousness. Yet, how different brain states emerge from the collective activity of brain regions remains unknown. Here, we studied the fMRI brain activity of monkeys during wakefulness and anesthesia-induced loss of consciousness. Using maximum entropy models, we derived collective, macroscopic properties that quantify the system’s capabilities to produce work, to contain information and to transmit it, and that indicate a phase transition from critical awake dynamics to supercritical anesthetized states. Moreover, information-theoretic measures identified those parameters that impacted the most the network dynamics. We found that changes in brain state and in state of consciousness primarily depended on changes in network couplings of insular, cingulate, and parietal cortices. Our findings suggest that the brain state transition underlying the loss of consciousness is predominantly driven by the uncoupling of specific brain regions from the rest of the network.

scholarly journals A Generalizable Multivariate Brain Pattern for Interpersonal Guilt

2020 ◽  
Vol 30 (6) ◽  
pp. 3558-3572 ◽  
Author(s):  
Hongbo Yu ◽  
Leonie Koban ◽  
Luke J Chang ◽  
Ullrich Wagner ◽  
Anjali Krishnan ◽  
...  
Keyword(s):  
Brain Activity ◽  
Test Sample ◽  
Training Sample ◽  
Brain State ◽  
Emotional States ◽  
Social Emotional ◽  
Social Emotions ◽  
Interpersonal Guilt ◽  
Experienced Pain ◽  
Independent Test Sample

Abstract Feeling guilty when we have wronged another is a crucial aspect of prosociality, but its neurobiological bases are elusive. Although multivariate patterns of brain activity show promise for developing brain measures linked to specific emotions, it is less clear whether brain activity can be trained to detect more complex social emotional states such as guilt. Here, we identified a distributed guilt-related brain signature (GRBS) across two independent neuroimaging datasets that used interpersonal interactions to evoke guilt. This signature discriminated conditions associated with interpersonal guilt from closely matched control conditions in a cross-validated training sample (N = 24; Chinese population) and in an independent test sample (N = 19; Swiss population). However, it did not respond to observed or experienced pain, or recalled guilt. Moreover, the GRBS only exhibited weak spatial similarity with other brain signatures of social-affective processes, further indicating the specificity of the brain state it represents. These findings provide a step toward developing biological markers of social emotions, which could serve as important tools to investigate guilt-related brain processes in both healthy and clinical populations.

scholarly journals State-dependent signatures of Anti-NMDA-Receptor Encephalitis: a dynamic functional connectivity study

2020 ◽  
Author(s):  
Nina von Schwanenflug ◽  
Stephan Krohn ◽  
Josephine Heine ◽  
Friedemann Paul ◽  
Harald Prüss ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Dwell Time ◽  
Large Scale ◽  
Brain Activity ◽  
Neuropsychiatric Symptoms ◽  
Autoimmune Disorder ◽  
Resting State Fmri ◽  
State Transitions ◽  
Group Status ◽  
Nmda Receptor Encephalitis

ABSTRACTIntroductionAnti-N-methyl-d-aspartate receptor encephalitis (NMDARE) is an autoimmune disorder associated with severe neuropsychiatric symptoms. While patients with NMDARE exhibit disrupted functional connectivity (FC), these findings have been limited to static connectivity analyses. This study applies time-resolved FC analysis to explore the temporal variability of large-scale brain activity in NMDARE and to assess the discriminatory power of functional brain states in a supervised classification approach.MethodsResting-state fMRI data from 57 patients with NMDARE and 61 controls was included. To capture brain dynamics, four discrete connectivity states were extracted and state-wise group differences in FC, occurrence, dwell time and transition frequency were assessed. Furthermore, logistic regression models with embedded feature selection were trained for each state to predict group status in a leave-one-out cross validation scheme.ResultsPatients showed FC alterations in three out of four states. Besides a reduction in hippocampal-frontal connectivity, we observed connectivity decreases within the default mode network and between frontal areas and subcortical as well as visual regions, which remained undetected in static FC. Furthermore, patients displayed a shift in dwell time from the weakly connected dominant state to a higher connected, but less frequent state, accompanied by increased transition frequencies. Discriminatory network features and predictive power varied dynamically over states, reaching up to 78.6% classification accuracy.ConclusionPatients showed state-specific alterations in FC along with a shift in dwell time and increased volatility of state transitions. These measures were associated with disease severity and duration, highlighting the potential of spatiotemporal dynamics in FC as prognostic biomarkers in NMDARE.

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