Uncovering hidden brain state dynamics that regulate performance and decision-making during cognition

Taghia, Jalil; Cai, Weidong; Ryali, Srikanth; Kochalka, John; Nicholas, Jonathan; Chen, Tianwen; Menon, Vinod

doi:10.1038/s41467-018-04723-6

Cited by 163 publications

(203 citation statements)

References 70 publications

(100 reference statements)

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“…It has been proposed that such three networks are the "core" neurocognitive networks and their functional interactions are important for complex, high-order cognitive functions (Menon, 2011) and could be responsible for psychiatric and neurological disorders, such as depression (Berman et al, 2011), schizophrenia (Palaniyappan et al, 2011), and dementia (Zhou et al, 2010;Yu et al, 2017). Recently, researchers have found that the dynamic network switch supports flexible attention (mainly via DAN and VAN) and cognitive control (mainly via FPN) to meet time-varying changes in cognitive demands, and the loss of such optimized dynamics may lead to poor task performance in a decision-making task (Taghia et al, 2018). Such a flexibility among the three networks could be associated with Attention-deficit/hyperactivity disorder (ADHD) and schizophrenia (Supekar et al, 2019).…”

Section: Increased Inter-network Fc Flexibilitymentioning

confidence: 99%

Development of Dynamic Functional Architecture during Early Infancy

Wen

Wang

Lin

et al. 2019

Preprint

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Understanding the moment-to-moment dynamics of functional connectivity (FC) in the human brain during early development is crucial for uncovering neuro-mechanisms of the emerging complex cognitive functions and behaviors.Instead of calculating FC in a static perspective, we leveraged a longitudinal resting-state functional magnetic resonances imaging dataset from fifty-one typically developing infants and, for the first time, thoroughly investigated how the temporal variability of the FC architecture develops at the global (entire brain), meso-(functional system) and local (brain region) levels in the first two years of age. Our results revealed that, in such a pivotal stage, 1) the whole-brain FC dynamics is linearly increased; 2) the high-order functional systems display increased FC dynamics for both within-and betweennetwork connections, while the primary systems show the opposite trajectories; 3) many frontal regions have increasing FC dynamics despite large heterogeneity in developmental trajectories and velocities. All these findings indicate that the brain is gradually reconfigured towards a more flexible, dynamic, and adaptive system with globally increasing but locally heterogeneous trajectories in the first two postnatal years, explaining why infants have emerging and rapidly developing high-order cognitive functions and complex behaviors.

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Section: Increased Inter-network Fc Flexibilitymentioning

confidence: 99%

Development of Dynamic Functional Architecture during Early Infancy

Wen

Wang

Lin

et al. 2019

Preprint

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“…The HMM has recently been applied to model neural dynamics inferred from magnetoencephalography (MEG) (Baker et al, 2014;Quinn et al, 2018;Vidaurre et al, 2016) as well as task-based and resting-state functional MRI data (Ryali et al, 2016;Vidaurre et al, 2017a;Vidaurre et al, 2017b;Taghia et al, 2018). These seminal studies provide evidence for the utility of the HMM in characterizing dynamic interactions between brain networks in healthy individuals and show that the frequency of state transitions increases with age (Ryali et al, 2016) and transitions can be organized hierarchically into cognitive and sensorimotor metastates, with dwell times in these putative metastases being heritable and correlating with cognitive traits (Vidaurre et al, 2017a).…”

Section: Introductionmentioning

confidence: 99%

Brain network dynamics in schizophrenia: Reduced dynamism of the default mode network

Kottaram

Johnston

Cocchi

et al. 2019

Human Brain Mapping

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Complex human behavior emerges from dynamic patterns of neural activity that transiently synchronize between distributed brain networks. This study aims to model the dynamics of neural activity in individuals with schizophrenia and to investigate whether the attributes of these dynamics associate with the disorder's behavioral and cognitive deficits. A hidden Markov model (HMM) was inferred from resting‐state functional magnetic resonance imaging (fMRI) data that was temporally concatenated across individuals with schizophrenia (n = 41) and healthy comparison individuals (n = 41). Under the HMM, fluctuations in fMRI activity within 14 canonical resting‐state networks were described using a repertoire of 12 brain states. The proportion of time spent in each state and the mean length of visits to each state were compared between groups, and canonical correlation analysis was used to test for associations between these state descriptors and symptom severity. Individuals with schizophrenia activated default mode and executive networks for a significantly shorter proportion of the 8‐min acquisition than healthy comparison individuals. While the default mode was activated less frequently in schizophrenia, the duration of each activation was on average 4–5 s longer than the comparison group. Severity of positive symptoms was associated with a longer proportion of time spent in states characterized by inactive default mode and executive networks, together with heightened activity in sensory networks. Furthermore, classifiers trained on the state descriptors predicted individual diagnostic status with an accuracy of 76–85%.

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“…A principled framework to tackle this issue is to model neural population dynamics using hidden Markov models (HMMs) [10]. These state space models can identify hidden states from population activity patterns in single trials, and have been successfully deployed in a variety of tasks and species from C. Elegans [5] to rodents [11][12][13][14], primates [15][16][17][18] and humans [19,20]. Hidden Markov models segment single-trial population activity into sequences in an unsupervised manner by inferring hidden states from multi-neuron firing patterns.…”

Section: Introductionmentioning

confidence: 99%

Metastable attractors explain the variable timing of stable behavioral action sequences

Recanatesi

Pereira

Murakami

et al. 2020

Preprint

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Natural animal behavior displays rich lexical and temporal dynamics, even in a stable environment. This implies that behavioral variability arises from sources within the brain, but the origin and mechanics of these processes remain largely unknown. Here, we focus on the observation that the timing of self-initiated actions shows large variability even when they are executed in stable, well-learned sequences. Could this mix of reliability and stochasticity arise within the same circuit? We trained rats to perform a stereotyped sequence of self-initiated actions and recorded neural ensemble activity in secondary motor cortex (M2), which is known to reflect trial-by-trial action timing fluctuations. Using hidden Markov models we established a robust and accurate dictionary between ensemble activity patterns and actions. We then showed that metastable attractors, representing activity patterns with the requisite combination of reliable sequential structure and high transition timing variability, could be produced by reciprocally coupling a high dimensional recurrent network and a low dimensional feedforward one. Transitions between attractors were generated by correlated variability arising from the feedback loop between the two networks. This mechanism predicted a specific structure of low-dimensional noise correlations that were empirically verified in M2 ensemble dynamics. This work suggests a robust network motif as a novel mechanism to support critical aspects of animal behavior and establishes a framework for investigating its circuit origins via correlated variability.

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Uncovering hidden brain state dynamics that regulate performance and decision-making during cognition

Cited by 163 publications

References 70 publications

Development of Dynamic Functional Architecture during Early Infancy

Development of Dynamic Functional Architecture during Early Infancy

Brain network dynamics in schizophrenia: Reduced dynamism of the default mode network

Metastable attractors explain the variable timing of stable behavioral action sequences

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