Increased cognitive complexity reveals abnormal brain network activity in individuals with corpus callosum dysgenesis

AbstractCognitive reasoning is thought to require functional interactions between whole-brain networks. Such networks rely on both cerebral hemispheres, with the corpus callosum providing cross-hemispheric communication. Here we used high-field functional magnetic resonance imaging (7T fMRI), a well validated cognitive task, and brain network analyses to investigate the functional networks underlying cognitive reasoning in individuals with corpus callosum dysgenesis (CCD), an anatomical abnormality that affects the corpus callosum. Participants with CCD were asked to solve cognitive reasoning problems while their brain activity was measured using fMRI. The complexity of these problems was parametrically varied by changing the complexity of relations that needed to be established between shapes within each problem matrix. Behaviorally, participants showed a typical reduction in task performance as problem complexity increased. Task-evoked neural activity was observed in brain regions known to constitute two key cognitive control systems: the fronto-parietal and cingulo-opercular networks. Under low complexity demands, network topology and the patterns of local neural activity in the CCD group closely resembled those observed in neurotypical controls. By contrast, when asked to solve more complex problems, participants with CCD showed a reduction in neural activity and connectivity within the fronto-parietal network. These complexity-induced, as opposed to resting-state, differences in functional network activity help resolve the apparent paradox between preserved network architecture found at rest in CCD individuals, and the heterogeneous deficits they display in response to cognitive task demands [preprint: https://doi.org/10.1101/312629].

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Increased cognitive complexity reveals abnormal brain network activity in individuals with corpus callosum dysgenesis

NeuroImage Clinical ◽

10.1016/j.nicl.2018.11.005 ◽

2019 ◽

Vol 21 ◽

pp. 101595 ◽

Cited By ~ 4

Author(s):

Luke J. Hearne ◽

Ryan J. Dean ◽

Gail A. Robinson ◽

Linda J. Richards ◽

Jason B. Mattingley ◽

...

Keyword(s):

Corpus Callosum ◽

Cognitive Complexity ◽

Brain Network ◽

Network Activity ◽

Abnormal Brain

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Identification of neural oscillations and epileptiform changes in human brain organoids

10.1101/820183 ◽

2019 ◽

Cited By ~ 3

Author(s):

Ranmal A. Samarasinghe ◽

Osvaldo A. Miranda ◽

Simon Mitchell ◽

Isabella Ferando ◽

Momoko Watanabe ◽

...

Keyword(s):

Neural Network ◽

Human Brain ◽

Network Architecture ◽

Network Formation ◽

Neurological Diseases ◽

Extracellular Recording ◽

Brain Network ◽

Network Activity ◽

Intact Brain ◽

Oscillatory Network

ABSTRACTHuman brain organoids represent a powerful tool for the study of human neurological diseases particularly those that impact brain growth and structure. However, many neurological diseases lack obvious anatomical abnormalities, yet significantly impact neural network functions, raising the question of whether organoids possess sufficient neural network architecture and complexity to model these conditions. Here, we explore the network level functions of brain organoids using calcium sensor imaging and extracellular recording approaches that together reveal the existence of complex oscillatory network behaviors reminiscent of intact brain preparations. We further demonstrate strikingly abnormal epileptiform network activity in organoids derived from a Rett Syndrome patient despite only modest anatomical differences from isogenically matched controls, and rescue with an unconventional neuromodulatory drug Pifithrin-α. Together, these findings provide an essential foundation for the utilization of human brain organoids to study intact and disordered human brain network formation and illustrate their utility in therapeutic discovery.

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Multi-Task Brain Network Reconfiguration is Inversely Associated with Human Intelligence

10.1101/2021.07.31.454563 ◽

2021 ◽

Author(s):

Jonas Alexander Thiele ◽

Joshua Faskowitz ◽

Olaf Sporns ◽

Kirsten Hilger

Keyword(s):

Cognitive Ability ◽

Network Architecture ◽

Brain Networks ◽

Brain Network ◽

Task Demands ◽

Human Intelligence ◽

Network Reconfiguration ◽

General Intelligence ◽

Functional Brain ◽

Functional Brain Networks

Intelligence describes the general cognitive ability level of a person. It is one of the most fundamental concepts in psychological science and is crucial for effective adaption of behavior to varying environmental demands. Changing external task demands have been shown to induce reconfiguration of functional brain networks. However, whether neural reconfiguration between different tasks is associated with intelligence has not yet been investigated. We used fMRI data from 812 subjects to show that higher scores of general intelligence are related to less brain network reconfiguration between resting state and seven different tasks as well as to network reconfiguration between tasks. This association holds for all functional brain networks except the motor system, and replicates in two independent samples (N = 138, N = 184). Our findings suggest that the intrinsic network architecture of individuals with higher general intelligence scores is closer to the network architecture as required by various cognitive demands. Multi-task brain network reconfiguration may, therefore, reflect the neural equivalent of the behavioral positive manifold, i.e., the essence of the concept of general intelligence. Finally, our results support neural efficiency theories of cognitive ability and reveal insights into human intelligence as an emergent property from a distributed multi-task brain network.

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Reconfiguration of functional brain networks and metabolic cost converge during task performance

eLife ◽

10.7554/elife.52443 ◽

2020 ◽

Vol 9 ◽

Cited By ~ 3

Author(s):

Andreas Hahn ◽

Michael Breakspear ◽

Lucas Rischka ◽

Wolfgang Wadsak ◽

Godber M Godbersen ◽

...

Keyword(s):

Cognitive Task ◽

Metabolic Cost ◽

Glucose Consumption ◽

Brain Network ◽

Task Engagement ◽

Functional Brain ◽

Network Analyses ◽

Complex Cognitive Task ◽

Functional Brain Network ◽

Brain Network Reorganization

The ability to solve cognitive tasks depends upon adaptive changes in the organization of whole-brain functional networks. However, the link between task-induced network reconfigurations and their underlying energy demands is poorly understood. We address this by multimodal network analyses integrating functional and molecular neuroimaging acquired concurrently during a complex cognitive task. Task engagement elicited a marked increase in the association between glucose consumption and functional brain network reorganization. This convergence between metabolic and neural processes was specific to feedforward connections linking the visual and dorsal attention networks, in accordance with task requirements of visuo-spatial reasoning. Further increases in cognitive load above initial task engagement did not affect the relationship between metabolism and network reorganization but only modulated existing interactions. Our findings show how the upregulation of key computational mechanisms to support cognitive performance unveils the complex, interdependent changes in neural metabolism and neuro-vascular responses.

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Integrated Brain Network Architecture Supports Cognitive Task Performance

Neuron ◽

10.1016/j.neuron.2016.10.004 ◽

2016 ◽

Vol 92 (2) ◽

pp. 278-279 ◽

Cited By ~ 5

Author(s):

Douglas H. Schultz ◽

Michael W. Cole

Keyword(s):

Task Performance ◽

Network Architecture ◽

Cognitive Task ◽

Brain Network ◽

Cognitive Task Performance

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Causal coupling between neural activity, metabolism, and behavior across the Drosophila brain

10.1101/2020.03.18.997742 ◽

2020 ◽

Author(s):

Kevin Mann ◽

Stephane Deny ◽

Surya Ganguli ◽

Thomas R. Clandinin

Keyword(s):

Energy Metabolism ◽

Neural Activity ◽

Large Scale ◽

Metabolic Flux ◽

Fruit Fly ◽

Brain Network ◽

Network Activity ◽

Tight Coupling ◽

Behavioral States ◽

The Brain

Coordinated activity across networks of neurons is a hallmark of both resting and active behavioral states in many species, including worms, flies, fish, mice and humans1–5. These global patterns alter energy metabolism in the brain over seconds to hours, making oxygen consumption and glucose uptake widely used proxies of neural activity6,7. However, whether changes in neural activity are causally related to changes in metabolic flux in intact circuits on the sub-second timescales associated with behavior, is unknown. Moreover, it is unclear whether transitions between rest and action are associated with spatiotemporally structured changes in neuronal energy metabolism. Here, we combine two-photon microscopy of the entire fruit fly brain with sensors that allow simultaneous measurements of neural activity and metabolic flux, across both resting and active behavioral states. We demonstrate that neural activity drives changes in metabolic flux, creating a tight coupling between these signals that can be measured across large-scale brain networks. Further, these studies reveal that the initiation of even minimal behavioral movements causes large-scale changes in the pattern of neural activity and energy metabolism, revealing unexpected structure in the functional architecture of the brain. The relationship between neural activity and energy metabolism is likely evolutionarily ancient. Thus, these studies provide a critical foundation for using metabolic proxies to capture changes in neural activity and reveal that even minimal behavioral movements are associated with changes in large-scale brain network activity.

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Characterizing the Network Architecture of Emotion Regulation Neurodevelopment

10.1101/773895 ◽

2019 ◽

Author(s):

João F. Guassi Moreira ◽

Katie A. McLaughlin ◽

Jennifer A. Silvers

Keyword(s):

Emotion Regulation ◽

Skill Acquisition ◽

Network Architecture ◽

Evolutionary Biology ◽

Brain Activity ◽

Brain Networks ◽

Adult Life ◽

Brain Network ◽

Network Activity ◽

Whole Brain

AbstractThe ability to regulate emotions is key to goal attainment and wellbeing. Although much has been discovered about how the human brain develops to support the acquisition of emotion regulation, very little of this work has leveraged information encoded in whole-brain networks. Here we employed a network neuroscience framework in conjunction with machine learning to: (i) parse the neural underpinnings of emotion regulation skill acquisition while accounting for age, and (ii) build a working taxonomy of brain network activity supporting emotion regulation in a sample of youth (N = 70, 34 female). We were able to predict emotion regulation ability, but not age, using network activity metrics from whole-brain networks during an emotion regulation task. Further, by leveraging analytic techniques traditionally used in evolutionary biology (e.g., cophenetic correlations), we were able to demonstrate that brain networks evince reliable taxonomic organization to meet emotion regulation demands in youth. This work shows that meaningful information about emotion regulation development is encoded in whole-brain network activity, suggesting that brain activity during emotion regulation encodes unique information about regulatory skill acquisition in youth but not domain-general maturation.Significance StatementThe acquisition of emotion regulation is critical for healthy functioning in later adult life. To date, little is known about how brain networks support the developmental acquisition of emotion regulation skills. This is noteworthy because brain networks have been increasingly shown to provide highly useful information about neural activity. Here we show that brain activity during an emotion regulation task encodes information about regulatory abilities over and above age. These results suggest emotion regulation skills are dependent on neural specialization of domain-specific systems, whereas age is encoded via domain-general systems.

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Synaptic Plasticity Shapes Brain Connectivity: Implications for Network Topology

International Journal of Molecular Sciences ◽

10.3390/ijms20246193 ◽

2019 ◽

Vol 20 (24) ◽

pp. 6193 ◽

Cited By ~ 9

Author(s):

Mario Stampanoni Bassi ◽

Ennio Iezzi ◽

Luana Gilio ◽

Diego Centonze ◽

Fabio Buttari

Keyword(s):

Synaptic Plasticity ◽

Neural Plasticity ◽

Network Architecture ◽

Brain Connectivity ◽

Network Connectivity ◽

Long Term Potentiation ◽

Brain Network ◽

Homeostatic Plasticity ◽

Network Activity ◽

Network Organization

Studies of brain network connectivity improved understanding on brain changes and adaptation in response to different pathologies. Synaptic plasticity, the ability of neurons to modify their connections, is involved in brain network remodeling following different types of brain damage (e.g., vascular, neurodegenerative, inflammatory). Although synaptic plasticity mechanisms have been extensively elucidated, how neural plasticity can shape network organization is far from being completely understood. Similarities existing between synaptic plasticity and principles governing brain network organization could be helpful to define brain network properties and reorganization profiles after damage. In this review, we discuss how different forms of synaptic plasticity, including homeostatic and anti-homeostatic mechanisms, could be directly involved in generating specific brain network characteristics. We propose that long-term potentiation could represent the neurophysiological basis for the formation of highly connected nodes (hubs). Conversely, homeostatic plasticity may contribute to stabilize network activity preventing poor and excessive connectivity in the peripheral nodes. In addition, synaptic plasticity dysfunction may drive brain network disruption in neuropsychiatric conditions such as Alzheimer’s disease and schizophrenia. Optimal network architecture, characterized by efficient information processing and resilience, and reorganization after damage strictly depend on the balance between these forms of plasticity.

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