scholarly journals Two dominant brain states reflect optimal and suboptimal attention

2020 ◽  
Author(s):  
Ayumu Yamashita ◽  
David Rothlein ◽  
Aaron Kucyi ◽  
Eve M. Valera ◽  
Michael Esterman
Keyword(s):  
Sustained Attention ◽  
Brain Activity ◽  
Activity Patterns ◽  
Mind Wandering ◽  
Network Activity ◽  
Brain State ◽  
Attention Task ◽  
Dorsal Attention Network ◽  
Brain States ◽  
The Difference

AbstractAttention is not constant but fluctuates from moment to moment. Previous studies dichotomized these fluctuations into optimal and suboptimal states based on behavioral performance and investigated the difference in brain activity between these states. Although these studies implicitly assume there are two states, this assumption is not guaranteed. Here, we reversed the logic of these previous studies and identified unique states of brain activity during a sustained attention task. We demonstrate a systematic relationship between dynamic brain activity patterns (brain states) and behavioral underpinnings of sustained attention by explaining behavior from two dominantly observed brain states. In four independent datasets, a brain state characterized by default mode network activity was behaviorally optimal and a brain state characterized by dorsal attention network activity was suboptimal. Thus, our study provides compelling evidence for behaviorally optimal and suboptimal attentional states from the sole viewpoint of brain activity. We further demonstrated how these brain states were impacted by motivation, mind wandering, and attention-deficit hyperactivity disorder. Within-subject level modulators (motivation and mind wandering) impacted the optimality of behavior in the suboptimal brain state. In contrast, between-subject level differences (ADHD vs healthy controls) impacted the optimal brain state character, namely its frequency.

Self-reported Mind Wandering and Response Time Variability Differentiate Prestimulus Electroencephalogram Microstate Dynamics during a Sustained Attention Task

2021 ◽  
Vol 33 (1) ◽  
pp. 28-45
Author(s):  
Anthony P. Zanesco ◽  
Ekaterina Denkova ◽  
Amishi P. Jha
Keyword(s):  
Sustained Attention ◽  
Temporal Dynamics ◽  
Response Times ◽  
Brain Activity ◽  
Mind Wandering ◽  
Time Variability ◽  
Attention Task ◽  
Brain States ◽  
Response Task ◽  
Task Focus

Brain activity continuously and spontaneously fluctuates during tasks of sustained attention. This spontaneous activity reflects the intrinsic dynamics of neurocognitive networks, which have been suggested to differentiate moments of externally directed task focus from episodes of mind wandering. However, the contribution of specific electrophysiological brain states and their millisecond dynamics to the experience of mind wandering is still unclear. In this study, we investigated the association between electroencephalogram microstate temporal dynamics and self-reported mind wandering. Thirty-six participants completed a sustained attention to response task in which they were asked to respond to frequently occurring upright faces (nontargets) and withhold responses to rare inverted faces (targets). Intermittently, experience sampling probes assessed whether participants were focused on the task or whether they were mind wandering (i.e., off-task). Broadband electroencephalography was recorded and segmented into a time series of brain electric microstates based on data-driven clustering of topographic voltage patterns. The strength, prevalence, and rate of occurrence of specific microstates differentiated on- versus off-task moments in the prestimulus epochs of trials preceding probes. Similar associations were also evident between microstates and variability in response times. Together, these findings demonstrate that distinct microstates and their millisecond dynamics are sensitive to the experience of mind wandering.

scholarly journals Temporal circuit of macroscale dynamic brain activity supports human consciousness

2020 ◽  
Vol 6 (11) ◽  
pp. eaaz0087 ◽  
Author(s):  
Zirui Huang ◽  
Jun Zhang ◽  
Jinsong Wu ◽  
George A. Mashour ◽  
Anthony G. Hudetz
Keyword(s):  
Default Mode Network ◽  
Functional Role ◽  
Brain Activity ◽  
Human Consciousness ◽  
Attention Network ◽  
Attention Networks ◽  
Default Mode ◽  
Dorsal Attention Network ◽  
Brain States

The ongoing stream of human consciousness relies on two distinct cortical systems, the default mode network and the dorsal attention network, which alternate their activity in an anticorrelated manner. We examined how the two systems are regulated in the conscious brain and how they are disrupted when consciousness is diminished. We provide evidence for a “temporal circuit” characterized by a set of trajectories along which dynamic brain activity occurs. We demonstrate that the transitions between default mode and dorsal attention networks are embedded in this temporal circuit, in which a balanced reciprocal accessibility of brain states is characteristic of consciousness. Conversely, isolation of the default mode and dorsal attention networks from the temporal circuit is associated with unresponsiveness of diverse etiologies. These findings advance the foundational understanding of the functional role of anticorrelated systems in 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.

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 Neuromodulation of the mind-wandering brain state: the interaction between neuromodulatory tone, sharp wave-ripples and spontaneous thought

2020 ◽  
Vol 376 (1817) ◽  
pp. 20190699 ◽  
Author(s):  
Claire O'Callaghan ◽  
Ishan C. Walpola ◽  
James M. Shine
Keyword(s):  
Large Scale ◽  
Mind Wandering ◽  
Brain State ◽  
Theme Issue ◽  
External Stimulation ◽  
Sharp Wave ◽  
Brain States ◽  
Spontaneous Thought ◽  
The Mind

Mind-wandering has become a captivating topic for cognitive neuroscientists. By now, it is reasonably well described in terms of its phenomenology and the large-scale neural networks that support it. However, we know very little about what neurobiological mechanisms trigger a mind-wandering episode and sustain the mind-wandering brain state. Here, we focus on the role of ascending neuromodulatory systems (i.e. acetylcholine, noradrenaline, serotonin and dopamine) in shaping mind-wandering. We advance the hypothesis that the hippocampal sharp wave-ripple (SWR) is a compelling candidate for a brain state that can trigger mind-wandering episodes. This hippocampal rhythm, which occurs spontaneously in quiescent behavioural states, is capable of propagating widespread activity in the default network and is functionally associated with recollective, associative, imagination and simulation processes. The occurrence of the SWR is heavily dependent on hippocampal neuromodulatory tone. We describe how the interplay of neuromodulators may promote the hippocampal SWR and trigger mind-wandering episodes. We then identify the global neuromodulatory signatures that shape the evolution of the mind-wandering brain state. Under our proposed framework, mind-wandering emerges due to the interplay between neuromodulatory systems that influence the transitions between brain states, which either facilitate, or impede, a wandering mind. This article is part of the theme issue ‘Offline perception: voluntary and spontaneous perceptual experiences without matching external stimulation'.

scholarly journals Variable rather than extreme slow reaction times distinguish brain states during sustained attention

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Ayumu Yamashita ◽  
David Rothlein ◽  
Aaron Kucyi ◽  
Eve M. Valera ◽  
Laura Germine ◽  
...  
Keyword(s):  
Sustained Attention ◽  
Reaction Times ◽  
Large Dataset ◽  
Attention Task ◽  
Long Tail ◽  
Behavioral Marker ◽  
Attention Focus ◽  
Brain States ◽  
Rt Distributions ◽  
Variance Factor

AbstractA common behavioral marker of optimal attention focus is faster responses or reduced response variability. Our previous study found two dominant brain states during sustained attention, and these states differed in their behavioral accuracy and reaction time (RT) variability. However, RT distributions are often positively skewed with a long tail (i.e., reflecting occasional slow responses). Therefore, a larger RT variance could also be explained by this long tail rather than the variance around an assumed normal distribution (i.e., reflecting pervasive response instability based on both faster and slower responses). Resolving this ambiguity is important for better understanding mechanisms of sustained attention. Here, using a large dataset of over 20,000 participants who performed a sustained attention task, we first demonstrated the utility of the exGuassian distribution that can decompose RTs into a strategy factor, a variance factor, and a long tail factor. We then investigated which factor(s) differed between the two brain states using fMRI. Across two independent datasets, results indicate unambiguously that the variance factor differs between the two dominant brain states. These findings indicate that ‘suboptimal’ is different from ‘slow’ at the behavior and neural level, and have implications for theoretically and methodologically guiding future sustained attention research.

scholarly journals Artifacts in EEG-Based BCI Therapies: Friend or Foe?

Sensors ◽  
2021 ◽  
Vol 22 (1) ◽  
pp. 96
Author(s):  
Eric James McDermott ◽  
Philipp Raggam ◽  
Sven Kirsch ◽  
Paolo Belardinelli ◽  
Ulf Ziemann ◽  
...  
Keyword(s):  
Classification Accuracy ◽  
Therapeutic Potential ◽  
Brain Activity ◽  
Mental States ◽  
Extraction Methods ◽  
Brain State ◽  
Learning Approaches ◽  
Eeg Signal ◽  
Motor Tasks ◽  
Brain States

EEG-based brain–computer interfaces (BCI) have promising therapeutic potential beyond traditional neurofeedback training, such as enabling personalized and optimized virtual reality (VR) neurorehabilitation paradigms where the timing and parameters of the visual experience is synchronized with specific brain states. While BCI algorithms are often designed to focus on whichever portion of a signal is most informative, in these brain-state-synchronized applications, it is of critical importance that the resulting decoder is sensitive to physiological brain activity representative of various mental states, and not to artifacts, such as those arising from naturalistic movements. In this study, we compare the relative classification accuracy with which different motor tasks can be decoded from both extracted brain activity and artifacts contained in the EEG signal. EEG data were collected from 17 chronic stroke patients while performing six different head, hand, and arm movements in a realistic VR-based neurorehabilitation paradigm. Results show that the artifactual component of the EEG signal is significantly more informative than brain activity with respect to classification accuracy. This finding is consistent across different feature extraction methods and classification pipelines. While informative brain signals can be recovered with suitable cleaning procedures, we recommend that features should not be designed solely to maximize classification accuracy, as this could select for remaining artifactual components. We also propose the use of machine learning approaches that are interpretable to verify that classification is driven by physiological brain states. In summary, whereas informative artifacts are a helpful friend in BCI-based communication applications, they can be a problematic foe in the estimation of physiological brain states.

scholarly journals The Relationship between Performance of Attention Task and Suicidal Ideation in Korean Patients with Mood Disorders

2020 ◽  
Vol 17 (4) ◽  
pp. 374-381
Author(s):  
Soo In Kim ◽  
Kyoung Ae Kong
Keyword(s):  
Suicidal Ideation ◽  
Mood Disorder ◽  
Sustained Attention ◽  
Test Performance ◽  
Predictive Ability ◽  
Depressive Mood ◽  
Attention Task ◽  
Psychological Variables ◽  
Attention Tasks ◽  
The Difference

Objective We examined the performance of attention tests related to suicidal ideation in mood disorder patients and to explain the difference of attention test performance in relation to suicidal ideation after controlling clinical and psychological variables of mood disorder patients.Methods Seventy-three in- and outpatients with major depressive disorder (n=41) or bipolar disorder (n=32) completed a self-rating questionnaire assessing socio-demographic characteristics, and clinical and psychological variables. Comprehensive Attention Test (CAT) also was conducted.Results Thirty-three patients were the high-suicidal ideation (SI) group, and forty patients were the low-SI group. The errors of commission (CEs) of visual sustained attention in the high-SI group was 6.3 times higher on average than that of the low-SI group. After controlling for sex, age, and diagnosis, a higher number of CEs on visual sustained attention tasks predicted higher SI score. However, after controlling for sex, age, diagnosis, and depressive mood, this predictive ability was no longer observed.Conclusion This study showed that CE on the visual sustained attention task seems to influence suicidal ideation as a result of interaction with depressive symptoms.

scholarly journals Context-dependent architecture of brain state dynamics is explained by white matter connectivity and theories of network control

2018 ◽  
Author(s):  
Eli J. Cornblath ◽  
Arian Ashourvan ◽  
Jason Z. Kim ◽  
Richard F. Betzel ◽  
Rastko Ciric ◽  
...  
Keyword(s):  
Working Memory ◽  
White Matter ◽  
Activity Patterns ◽  
Network Control ◽  
Membrane Properties ◽  
Neuronal Membrane ◽  
Brain State ◽  
Cognitive States ◽  
Brain States ◽  
Context Dependent

A diverse white matter network and finely tuned neuronal membrane properties allow the brain to transition seamlessly between cognitive states. However, it remains unclear how static structural connections guide the temporal progression of large-scale brain activity patterns in different cognitive states. Here, we deploy an unsupervised machine learning algorithm to define brain states as time point level activity patterns from functional magnetic resonance imaging data acquired during passive visual fixation (rest) and an n-back working memory task. We find that brain states are composed of interdigitated functional networks and exhibit context-dependent dynamics. Using diffusion-weighted imaging acquired from the same subjects, we show that structural connectivity constrains the temporal progression of brain states. We also combine tools from network control theory with geometrically conservative null models to demonstrate that brains are wired to support states of high activity in default mode areas, while requiring relatively low energy. Finally, we show that brain state dynamics change throughout development and explain working memory performance. Overall, these results elucidate the structural underpinnings of cognitively and developmentally relevant spatiotemporal brain dynamics.

scholarly journals States and traits of neural irregularity in the age-varying human brain

2017 ◽  
Author(s):  
Leonhard Waschke ◽  
Malte Wöestmann ◽  
Jonas Obleser
Keyword(s):  
Brain Activity ◽  
Physical World ◽  
Permutation Entropy ◽  
Brain State ◽  
Information Theoretic ◽  
Age Related ◽  
Brain States ◽  
Sensory Encoding ◽  
Physically Identical ◽  
Human Electroencephalogram

AbstractSensory representations of the physical world and thus human percepts are susceptible to fluctuations in brain state or “neural irregularity”. Furthermore, aging brains display altered levels of irregularity. We here show that a single, within-trial information-theoretic measure (weighted permutation entropy) captures neural irregularity in the human electroencephalogram as a proxy for both, trait-like differences between individuals of varying age, and state-like fluctuations that bias perceptual decisions. First, the overall level of neural irregularity increased with participants‘ age, paralleled by a decrease in variability over time, likely indexing age-related disintegration on structural and functional levels of brain activity. Second, states of higher neural irregularity were associated with optimized sensory encoding and a subsequently increased probability of choosing the first of two physically identical stimuli. In sum, neural irregularity not only characterizes behaviorally relevant brain states, but also can identify trait-like changes that come with age.

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