scholarly journals Avalanche criticality in individuals, fluid intelligence and working memory

2020 ◽  
Author(s):  
Longzhou Xu ◽  
Lianchun Yu ◽  
Jianfeng Feng
Keyword(s):  
Working Memory ◽  
Cognitive Performance ◽  
Large Scale ◽  
Fluid Intelligence ◽  
Phase Synchronization ◽  
Brain Networks ◽  
Regional Analysis ◽  
Neural Dynamics ◽  
Transition Diagram ◽  
Disorder Phase Transition

AbstractThe critical brain hypothesis suggests that efficient neural computation can be realized by neural dynamics characterized by a scale-free avalanche activity. However, the relation between human cognitive performance and the avalanche criticality in large-scale brain networks remains unclear. In this study, we used the phase synchronization analysis to determine the location of individual brains in the order-disorder phase transition diagram. We then performed avalanche analysis to identify subjects whose brain dynamics are close to the criticality. We showed that complexity in functional connectivity, as well as structure-function coupling, is maximized around criticality, inconsistent with theory predictions. Finally, we showed evidence that the neural dynamics of human participants with higher fluid intelligence and working memory scores are closer to criticality. The regional analysis showed it is the prefrontal cortex and inferior parietal cortex whose critical dynamics exhibit significant positive correlations with human cognitive performance. Our findings suggested that large-scale brain networks operate around a critical point to optimize human cognitive performance.

scholarly journals Activity-dependent myelination: A glial mechanism of oscillatory self-organization in large-scale brain networks

2020 ◽  
Vol 117 (24) ◽  
pp. 13227-13237 ◽  
Author(s):  
Rabiya Noori ◽  
Daniel Park ◽  
John D. Griffiths ◽  
Sonya Bells ◽  
Paul W. Frankland ◽  
...  
Keyword(s):  
White Matter ◽  
Large Scale ◽  
Phase Synchronization ◽  
Brain Networks ◽  
Self Organization ◽  
Conduction Delay ◽  
Neural Communication ◽  
Wide Range ◽  
The Impact ◽  
Activity Dependent

Communication and oscillatory synchrony between distributed neural populations are believed to play a key role in multiple cognitive and neural functions. These interactions are mediated by long-range myelinated axonal fiber bundles, collectively termed as white matter. While traditionally considered to be static after development, white matter properties have been shown to change in an activity-dependent way through learning and behavior—a phenomenon known as white matter plasticity. In the central nervous system, this plasticity stems from oligodendroglia, which form myelin sheaths to regulate the conduction of nerve impulses across the brain, hence critically impacting neural communication. We here shift the focus from neural to glial contribution to brain synchronization and examine the impact of adaptive, activity-dependent changes in conduction velocity on the large-scale phase synchronization of neural oscillators. Using a network model based on primate large-scale white matter neuroanatomy, our computational and mathematical results show that such plasticity endows white matter with self-organizing properties, where conduction delay statistics are autonomously adjusted to ensure efficient neural communication. Our analysis shows that this mechanism stabilizes oscillatory neural activity across a wide range of connectivity gain and frequency bands, making phase-locked states more resilient to damage as reflected by diffuse decreases in connectivity. Critically, our work suggests that adaptive myelination may be a mechanism that enables brain networks with a means of temporal self-organization, resilience, and homeostasis.

Individual T1-weighted/T2-weighted ratio brain networks: Small-worldness, hubs and modular organization

2018 ◽  
Vol 29 (05) ◽  
pp. 1840007
Author(s):  
Huijun Wu ◽  
Hao Wang ◽  
Linyuan Lü
Keyword(s):  
Large Scale ◽  
Fluid Intelligence ◽  
Brain Networks ◽  
Structural Mri ◽  
Modular Organization ◽  
Enormous Amount ◽  
Positive Correlations ◽  
Law Degree ◽  
Network Metrics ◽  
The Brain

Applying network science to investigate the complex systems has become a hot topic. In neuroscience, understanding the architectures of complex brain networks was a vital issue. An enormous amount of evidence had supported the brain was cost/efficiency trade-off with small-worldness, hubness and modular organization through the functional MRI and structural MRI investigations. However, the T1-weighted/T2-weighted (T1w/T2w) ratio brain networks were mostly unexplored. Here, we utilized a KL divergence-based method to construct large-scale individual T1w/T2w ratio brain networks and investigated the underlying topological attributes of these networks. Our results supported that the T1w/T2w ratio brain networks were comprised of small-worldness, an exponentially truncated power–law degree distribution, frontal-parietal hubs and modular organization. Besides, there were significant positive correlations between the network metrics and fluid intelligence. Thus, the T1w/T2w ratio brain networks open a new avenue to understand the human brain and are a necessary supplement for future MRI studies.

scholarly journals Model Based Inference of Large Scale Brain Networks with Approximate Bayesian Computation

2019 ◽  
Author(s):  
Timothy O. West ◽  
Luc Berthouze ◽  
Simon F. Farmer ◽  
Hayriye Cagnan ◽  
Vladimir Litvak
Keyword(s):  
Large Scale ◽  
Approximate Bayesian Computation ◽  
Model Comparison ◽  
Brain Networks ◽  
Face Validity ◽  
Inverse Modelling ◽  
Neural Dynamics ◽  
Bayesian Computation ◽  
Bayesian Model Comparison ◽  
Approximate Bayesian

AbstractBrain networks and the neural dynamics that unfold upon them are of great interest across the many scales of systems neuroscience. The tools of inverse modelling provide a way of both constraining and selecting models of large scale brain networks from empirical data. Such models have the potential to yield broad theoretical insights in the understanding of the physiological processes behind the integration and segregation of activity in the brain. In order to make inverse modelling computationally tractable, simplifying model assumptions have often been adopted that appeal to steady-state approximations to neural dynamics and thus prevent the investigation of stochastic or intermittent dynamics such as gamma or beta burst activity. In this work we describe a framework that uses the Approximate Bayesian Computation (ABC) algorithm for the inversion of neural models that can flexibly represent any statistical feature of empirically recorded data and eschew the need to assume a locally linearized system. Further, we demonstrate how Bayesian model comparison can be applied to fitted models to enable the selection of competing hypotheses regarding the causes of neural data. This work establishes a validation of the procedures by testing for both the face validity (i.e. the ability to identify the original model that has generated the observed data) and predictive validity (i.e. the consistency of the parameter estimation across multiple realizations of the same data). From the validation and example applications presented here we conclude that the proposed framework provides a novel opportunity to researchers aiming to explain how complex brain dynamics emerge from neural circuits.

scholarly journals Integrative Functional Network Interactions Underlie the Association between Physical Activity and Cognition in Neurodegenerative Diseases

2019 ◽  
Author(s):  
Chia-Hao Shih ◽  
Miriam Sklerov ◽  
Nina Browner ◽  
Eran Dayan
Keyword(s):  
Physical Activity ◽  
Neurodegenerative Diseases ◽  
Cognitive Decline ◽  
Cognitive Performance ◽  
Large Scale ◽  
Healthy Aging ◽  
Brain Networks ◽  
Neural Substrates ◽  
Functional Interactions ◽  
False Discovery

Physical activity (PA) has preventive and possibly restorative effects in aging-related cognitive decline, which relate to intrinsic functional interactions (functional connectivity, FC) in large-scale brain networks. Preventive and ameliorative effects of PA on cognitive decline have also been documented in neurodegenerative diseases, such as Parkinson's disease (PD). However, the neural substrates that mediate the association between PA and cognitive performance under such neurological conditions remain unknown. Here we set out to examine if the association between PA and cognitive performance in PD is mediated by FC in large-scale sensorimotor and association brain networks. Data from 51 PD patients were analyzed. Connectome-level analysis based on a whole-brain parcellation showed that self-reported levels of PA were associated with increased FC between, but not within the default mode (DMN) and salience networks (SAL) (p < .05, false discovery rate corrected). Additionally, multiple parallel mediation analysis further demonstrated that FC between left lateral parietal nodes in the DMN and rostral prefrontal nodes in the SAL mediated the association between PA and executive function performance. These findings are in line with previous studies linking FC in large-scale association networks with the effects of PA on cognition in healthy aging. Our results extend these previous results by demonstrating that the association between PA and cognitive performance in neurodegenerative diseases such as PD is mediated by integrative functional interactions in large-scale association networks.

scholarly journals Is baseline pupil size related to cognitive ability? Yes (under proper lighting conditions).

2020 ◽  
Author(s):  
Jason S. Tsukahara ◽  
Randall W Engle
Keyword(s):  
Working Memory ◽  
Individual Differences ◽  
Cognitive Ability ◽  
Pupil Size ◽  
Large Scale ◽  
Fluid Intelligence ◽  
Functional Organization ◽  
Working Memory Capacity ◽  
Memory Capacity ◽  
Lighting Conditions

We found that individual differences in baseline pupil size correlated with fluid intelligence and working memory capacity. Larger pupil size was associated with higher cognitive ability. However, other researchers have not been able to replicate our 2016 finding – though they only measured working memory capacity and not fluid intelligence. In a reanalysis of Tsukahara et al. (2016) we show that reduced variability on baseline pupil size will result in a higher probability of obtaining smaller and non-significant correlations with working memory capacity. In two large-scale studies, we demonstrated that reduced variability in baseline pupil size values was due to the monitor being too bright. Additionally, fluid intelligence and working memory capacity did correlate with baseline pupil size except in the brightest lighting conditions. Overall, our findings demonstrated that the baseline pupil size – working memory capacity relationship was not as strong or robust as that with fluid intelligence. Our findings have strong methodological implications for researchers investigating individual differences in task-free or task-evoked pupil size. We conclude that fluid intelligence does correlate with baseline pupil size and that this is related to the functional organization of the resting-state brain through the locus coeruleus-norepinephrine system.

scholarly journals Working memory networks and activation patterns in schizophrenia and bipolar disorder: comparison with healthy controls

2014 ◽  
Vol 204 (4) ◽  
pp. 290-298 ◽  
Author(s):  
Christine Lycke Brandt ◽  
Tom Eichele ◽  
Ingrid Melle ◽  
Kjetil Sundet ◽  
Andrés Server ◽  
...  
Keyword(s):  
Bipolar Disorder ◽  
Working Memory ◽  
Large Scale ◽  
Psychotic Disorders ◽  
Brain Networks ◽  
Memory Task ◽  
Control Group ◽  
Healthy Controls ◽  
Activation Patterns ◽  
Patient Groups

BackgroundSchizophrenia and bipolar disorder are severe mental disorders with overlapping genetic and clinical characteristics, including cognitive impairments. An important question is whether these disorders also have overlapping neuronal deficits.AimsTo determine whether large-scale brain networks associated with working memory, as measured with functional magnetic resonance imaging (fMRI), are the same in both schizophrenia and bipolar disorder, and how they differ from those in healthy individuals.MethodPatients with schizophrenia (n = 100) and bipolar disorder (n = 100) and a healthy control group (n = 100) performed a 2-back working memory task while fMRI data were acquired. The imaging data were analysed using independent component analysis to extract large-scale networks of task-related activations.ResultsSimilar working memory networks were activated in all groups. However, in three out of nine networks related to the experimental task there was a graded response difference in fMRI signal amplitudes, where patients with schizophrenia showed greater activation than those with bipolar disorder, who in turn showed more activation than healthy controls. Secondary analysis of the patient groups showed that these activation patterns were associated with history of psychosis and current elevated mood in bipolar disorder.ConclusionsThe same brain networks were related to working memory in schizophrenia, bipolar disorder and controls. However, some key networks showed a graded hyperactivation in the two patient groups, in line with a continuum of neuronal abnormalities across psychotic disorders.

scholarly journals Working Memory and Reasoning Benefit from Different Modes of Large-scale Brain Dynamics in Healthy Older Adults

2018 ◽  
Vol 30 (7) ◽  
pp. 1033-1046 ◽  
Author(s):  
Alexander V. Lebedev ◽  
Jonna Nilsson ◽  
Martin Lövdén
Keyword(s):  
Working Memory ◽  
Large Scale ◽  
Fluid Intelligence ◽  
Working Memory Training ◽  
Superior Performance ◽  
Memory Training ◽  
Reasoning Task ◽  
Healthy Older Adults ◽  
Training Interventions ◽  
Complex Reasoning

Researchers have proposed that solving complex reasoning problems, a key indicator of fluid intelligence, involves the same cognitive processes as solving working memory tasks. This proposal is supported by an overlap of the functional brain activations associated with the two types of tasks and by high correlations between interindividual differences in performance. We replicated these findings in 53 older participants but also showed that solving reasoning and working memory problems benefits from different configurations of the functional connectome and that this dissimilarity increases with a higher difficulty load. Specifically, superior performance in a typical working memory paradigm ( n-back) was associated with upregulation of modularity (increased between-network segregation), whereas performance in the reasoning task was associated with effective downregulation of modularity. We also showed that working memory training promotes task-invariant increases in modularity. Because superior reasoning performance is associated with downregulation of modular dynamics, training may thus have fostered an inefficient way of solving the reasoning tasks. This could help explain why working memory training does little to promote complex reasoning performance. The study concludes that complex reasoning abilities cannot be reduced to working memory and suggests the need to reconsider the feasibility of using working memory training interventions to attempt to achieve effects that transfer to broader cognition.

scholarly journals Dynamic reconfiguration of frontal brain networks during executive cognition in humans

2015 ◽  
Vol 112 (37) ◽  
pp. 11678-11683 ◽  
Author(s):  
Urs Braun ◽  
Axel Schäfer ◽  
Henrik Walter ◽  
Susanne Erk ◽  
Nina Romanczuk-Seiferth ◽  
...  
Keyword(s):  
Working Memory ◽  
Cognitive Function ◽  
Large Scale ◽  
Brain Networks ◽  
Dynamic Network ◽  
Dynamic Reconfiguration ◽  
Brain Regions ◽  
Evolving Networks ◽  
Network Flexibility ◽  
Frontal Systems

The brain is an inherently dynamic system, and executive cognition requires dynamically reconfiguring, highly evolving networks of brain regions that interact in complex and transient communication patterns. However, a precise characterization of these reconfiguration processes during cognitive function in humans remains elusive. Here, we use a series of techniques developed in the field of “dynamic network neuroscience” to investigate the dynamics of functional brain networks in 344 healthy subjects during a working-memory challenge (the “n-back” task). In contrast to a control condition, in which dynamic changes in cortical networks were spread evenly across systems, the effortful working-memory condition was characterized by a reconfiguration of frontoparietal and frontotemporal networks. This reconfiguration, which characterizes “network flexibility,” employs transient and heterogeneous connectivity between frontal systems, which we refer to as “integration.” Frontal integration predicted neuropsychological measures requiring working memory and executive cognition, suggesting that dynamic network reconfiguration between frontal systems supports those functions. Our results characterize dynamic reconfiguration of large-scale distributed neural circuits during executive cognition in humans and have implications for understanding impaired cognitive function in disorders affecting connectivity, such as schizophrenia or dementia.

scholarly journals Metastable neural dynamics underlies cognitive performance across multiple behavioural paradigms

2019 ◽  
Author(s):  
Thomas H. Alderson ◽  
Arun L.W. Bokde ◽  
J.A.Scott. Kelso ◽  
Liam Maguire ◽  
Damien Coyle
Keyword(s):  
Cognitive Control ◽  
Cognitive Performance ◽  
Resting State ◽  
Network Architecture ◽  
Large Scale ◽  
Fluid Intelligence ◽  
Coordination Dynamics ◽  
Resting State Networks ◽  
Control Networks ◽  
Local Areas

AbstractDespite resting state networks being associated with a variety of cognitive abilities, it remains unclear how these local areas act in concert to express particular cognitive operations. Theoretical and empirical accounts indicate that large-scale resting state networks reconcile dual tendencies toward integration and segregation by operating in a metastable regime of their coordination dynamics. One proposal is that metastability confers important behavioural qualities by dynamically binding distributed local areas into large-scale neurocognitive entities. We tested this hypothesis by analysing fMRI data in a large cohort of healthy individuals (N=566) and comparing the metastability of the brain’s large-scale resting network architecture at rest and during the performance of several tasks. Task-based reasoning was principally characterised by high metastability in cognitive control networks and low metastability in sensory processing areas. Although metastability between resting state networks increased during task performance, cognitive ability was more closely linked to spontaneous activity. High metastability in the intrinsic connectivity of cognitive control networks was linked to novel problem solving (or fluid intelligence) but was less important in tasks relying on prior experience (or crystallised intelligence). Crucially, subjects with resting architectures similar or ‘pre-configured’ to a task-general arrangement demonstrated superior cognitive performance. Taken together, our findings support a critical linkage between the spontaneous metastability of the large-scale networks of the cerebral cortex and cognition.

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