Structural control energy of resting‐state functional brain states reveals less cost‐effective brain dynamics in psychosis vulnerability

Sports-related concussion in youth is a major public health issue. Evaluating the diffuse and often subtle changes in structure and function that occur in the brain, particularly in this population, remains a significant challenge. The goal of this pilot study was to evaluate the relationship between the intrinsic dynamics of the brain using resting-state functional magnetic resonance imaging (rs-fMRI) and relate these findings to structural brain correlates from diffusion tensor imaging in a group of adolescents with sports-related concussions ( n = 6) and a group of healthy adolescent athletes ( n = 6). We analyzed rs-fMRI data using a sliding windows approach and related the functional findings to structural brain correlates by applying graph theory analysis to the diffusion tensor imaging data. Within the resting-state condition, we extracted three separate brain states in both groups. Our analysis revealed that the brain dynamics in healthy adolescents was characterized by a dynamic pattern, shifting equally between three brain states; however, in adolescents with concussion, the pattern was more static with a longer time spent in one brain state. Importantly, this lack of dynamic flexibility in the concussed group was associated with increased nodal strength in the left middle frontal gyrus, suggesting reorganization in a region related to attention. This preliminary report shows that both the intrinsic brain dynamics and structural organization are altered in networks related to attention in adolescents with concussion. This first report in adolescents will be used to inform future studies in a larger cohort.

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Structural control energy of resting-state functional brain states reveals inefficient brain dynamics in psychosis vulnerability

10.1101/703561 ◽

2019 ◽

Cited By ~ 2

Author(s):

Daniela Zöller ◽

Corrado Sandini ◽

Marie Schaer ◽

Stephan Eliez ◽

Danielle S. Bassett ◽

...

Keyword(s):

Resting State ◽

Structural Control ◽

Brain Activity ◽

Neurodevelopmental Disorder ◽

Structural Connectivity ◽

Negative Relationship ◽

Magnetic Resonance Images ◽

Network Control ◽

Deletion Syndrome ◽

Brain States

AbstractHow the brain’s white-matter anatomy constrains brain activity is an open question that might give insights into the mechanisms that underlie mental disorders such as schizophrenia. Chromosome 22q11.2 deletion syndrome (22q11DS) is a neurodevelopmental disorder with an extremely high risk for psychosis providing a test case to study developmental aspects of schizophrenia. In this study, we used principles from network control theory to probe the implications of aberrant structural connectivity for the brain’s functional dynamics in 22q11DS. We retrieved brain states from resting-state functional magnetic resonance images of 78 patients with 22q11DS and 85 healthy controls. Then, we compared them in terms of persistence control energy; i.e., the control energy that would be required to persist in each of these states based on individual structural connectivity and a dynamic model. Persistence control energy was altered in a broad pattern of brain states including both energetically more demanding and less demanding brain states in 22q11DS. Further, we found a negative relationship between persistence control energy and resting-state activation time, which suggests that the brain reduces energy by spending less time in energetically demanding brain states. In patients with 22q11DS, this behavior was less pronounced, suggesting a dynamic inefficiency of brain function in the disease. In summary, our results provide initial insights into the dynamic implications of altered structural connectivity in 22q11DS, which might improve our understanding of the mechanisms underlying the disease.

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Hierarchical disruption in the cortex of anesthetized monkeys as a new signature of consciousness loss

10.1101/2020.06.04.133538 ◽

2020 ◽

Author(s):

Camilo Miguel Signorelli ◽

Lynn Uhrig ◽

Morten Kringelbach ◽

Bechir Jarraya ◽

Gustavo Deco

Keyword(s):

Neural Activity ◽

Resting State ◽

Resting State Fmri ◽

Hierarchical Organization ◽

Brain Dynamics ◽

Depth Of Anesthesia ◽

Functional Changes ◽

Cortical Hierarchy ◽

Brain States ◽

Structure Similarity

AbstractAnesthesia induces a reconfiguration of the repertoire of functional brain states leading to a high function-structure similarity. However, it is unclear how these functional changes lead to loss of consciousness. Here we suggest that the mechanism of conscious access is related to a general dynamical rearrangement of the intrinsic hierarchical organization of the cortex. To measure cortical hierarchy, we applied the Intrinsic Ignition analysis to resting-state fMRI data acquired in awake and anesthetized macaques. Our results reveal the existence of spatial and temporal hierarchical differences of neural activity within the macaque cortex, with a strong modulation by the depth of anesthesia and the employed anesthetic agent. Higher values of Intrinsic Ignition correspond to rich and flexible brain dynamics whereas lower values correspond to poor and rigid, structurally driven brain dynamics. Moreover, spatial and temporal hierarchical dimensions are disrupted in a different manner, involving different hierarchical brain networks. All together suggest that disruption of brain hierarchy is a new signature of consciousness loss.

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Resting-state Dynamics as a Cortical Signature of Anesthesia in Monkeys

Anesthesiology ◽

10.1097/aln.0000000000002336 ◽

2018 ◽

Vol 129 (5) ◽

pp. 942-958 ◽

Cited By ~ 31

Author(s):

Lynn Uhrig ◽

Jacobo D. Sitt ◽

Amaury Jacob ◽

Jordy Tasserie ◽

Pablo Barttfeld ◽

...

Keyword(s):

Resting State ◽

Magnetic Resonance Images ◽

Brain Anatomy ◽

Loss Of Consciousness ◽

Function Structure ◽

Functional Brain ◽

Cluster Dynamic ◽

Brain States ◽

Structure Similarity ◽

The Brain

Abstract Editor’s Perspective What We Already Know about This Topic What This Article Tells Us That Is New Background The mechanism by which anesthetics induce a loss of consciousness remains a puzzling problem. We hypothesized that a cortical signature of anesthesia could be found in an increase in similarity between the matrix of resting-state functional correlations and the anatomical connectivity matrix of the brain, resulting in an increased function-structure similarity. Methods We acquired resting-state functional magnetic resonance images in macaque monkeys during wakefulness (n = 3) or anesthesia with propofol (n = 3), ketamine (n = 3), or sevoflurane (n = 3). We used the k-means algorithm to cluster dynamic resting-state data into independent functional brain states. For each condition, we performed a regression analysis to quantify function-structure similarity and the repertoire of functional brain states. Results Seven functional brain states were clustered and ranked according to their similarity to structural connectivity, with higher ranks corresponding to higher function-structure similarity and lower ranks corresponding to lower correlation between brain function and brain anatomy. Anesthesia shifted the brain state composition from a low rank (rounded rank [mean ± SD]) in the awake condition (awake rank = 4 [3.58 ± 1.03]) to high ranks in the different anesthetic conditions (ketamine rank = 6 [6.10 ± 0.32]; moderate propofol rank = 6 [6.15 ± 0.76]; deep propofol rank = 6 [6.16 ± 0.46]; moderate sevoflurane rank = 5 [5.10 ± 0.81]; deep sevoflurane rank = 6 [5.81 ± 1.11]; P < 0.0001). Conclusions Whatever the molecular mechanism, anesthesia led to a massive reconfiguration of the repertoire of functional brain states that became predominantly shaped by brain anatomy (high function-structure similarity), giving rise to a well-defined cortical signature of anesthesia-induced loss of consciousness.

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Non-linear manifold learning in fMRI uncovers a low-dimensional space of brain dynamics

10.1101/2020.11.25.398693 ◽

2020 ◽

Author(s):

Siyuan Gao ◽

Gal Mishne ◽

Dustin Scheinost

Keyword(s):

Resting State ◽

Dimensional Space ◽

Linear Manifold ◽

Fmri Data ◽

Cognitive Tasks ◽

Brain Dynamics ◽

State Data ◽

Non Linear ◽

Brain States ◽

Low Dimensional

Large-scale brain dynamics are believed to lie in a latent, low-dimensional space. Typically, the embeddings of brain scans are derived independently from different cognitive tasks or resting-state data, ignoring a potentially large-and shared-portion of this space. Here, we establish that a shared, robust, and interpretable low-dimensional space of brain dynamics can be recovered from a rich repertoire of task-based fMRI data. This occurs when relying on non-linear approaches as opposed to traditional linear methods. The embedding maintains proper temporal progression of the tasks, revealing brain states and the dynamics of network integration. We demonstrate that resting-state data embeds fully onto the same task embedding, indicating similar brain states are present in both task and resting-state data. Our findings suggest analysis of fMRI data from multiple cognitive tasks in a low-dimensional space is possible and desirable, and our proposed framework can thus provide an interpretable framework to investigate brain dynamics in the low-dimensional space.

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Intra and inter: Alterations in functional brain resting‐state networks after peripheral nerve injury

Brain and Behavior ◽

10.1002/brb3.1747 ◽

2020 ◽

Vol 10 (9) ◽

Author(s):

Xiang‐Xin Xing ◽

Xu‐Yun Hua ◽

Mou‐Xiong Zheng ◽

Zhen‐Zhen Ma ◽

Bei‐Bei Huo ◽

...

Keyword(s):

Peripheral Nerve ◽

Nerve Injury ◽

Resting State ◽

Peripheral Nerve Injury ◽

Resting State Networks ◽

Functional Brain

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Analysis of Functional Brain Network in MDD based on Improved Empirical Mode Decomposition with Resting State EEG Data

IEEE Transactions on Neural Systems and Rehabilitation Engineering ◽

10.1109/tnsre.2021.3092140 ◽

2021 ◽

pp. 1-1

Author(s):

Xuexiao Shao ◽

Shuting Sun ◽

Jianxiu Li ◽

Wenwen Kong ◽

Jing Zhu ◽

...

Keyword(s):

Empirical Mode Decomposition ◽

Resting State ◽

Brain Network ◽

Functional Brain ◽

Mode Decomposition ◽

Functional Brain Network ◽

Eeg Data

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Association between metabolic syndrome and resting-state functional brain connectivity

Neurobiology of Aging ◽

10.1016/j.neurobiolaging.2021.03.012 ◽

2021 ◽

Author(s):

Barnaly Rashid ◽

Victoria N. Poole ◽

Francesca C. Fortenbaugh ◽

Michael Esterman ◽

William P. Milberg ◽

...

Keyword(s):

Metabolic Syndrome ◽

Resting State ◽

Brain Connectivity ◽

Functional Brain

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Effects of a Motor Imagery Task on Functional Brain Network Community Structure in Older Adults: Data from the Brain Networks and Mobility Function (B-NET) Study

Brain Sciences ◽

10.3390/brainsci11010118 ◽

2021 ◽

Vol 11 (1) ◽

pp. 118

Author(s):

Blake R. Neyland ◽

Christina E. Hugenschmidt ◽

Robert G. Lyday ◽

Jonathan H. Burdette ◽

Laura D. Baker ◽

...

Keyword(s):

Older Adults ◽

Graph Theory ◽

Community Structure ◽

Resting State ◽

Brain Networks ◽

Brain Network ◽

Functional Brain ◽

Network Community ◽

Functional Brain Networks ◽

The Brain

Elucidating the neural correlates of mobility is critical given the increasing population of older adults and age-associated mobility disability. In the current study, we applied graph theory to cross-sectional data to characterize functional brain networks generated from functional magnetic resonance imaging data both at rest and during a motor imagery (MI) task. Our MI task is derived from the Mobility Assessment Tool–short form (MAT-sf), which predicts performance on a 400 m walk, and the Short Physical Performance Battery (SPPB). Participants (n = 157) were from the Brain Networks and Mobility (B-NET) Study (mean age = 76.1 ± 4.3; % female = 55.4; % African American = 8.3; mean years of education = 15.7 ± 2.5). We used community structure analyses to partition functional brain networks into communities, or subnetworks, of highly interconnected regions. Global brain network community structure decreased during the MI task when compared to the resting state. We also examined the community structure of the default mode network (DMN), sensorimotor network (SMN), and the dorsal attention network (DAN) across the study population. The DMN and SMN exhibited a task-driven decline in consistency across the group when comparing the MI task to the resting state. The DAN, however, displayed an increase in consistency during the MI task. To our knowledge, this is the first study to use graph theory and network community structure to characterize the effects of a MI task, such as the MAT-sf, on overall brain network organization in older adults.

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