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

2019 ◽  
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.

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

2021 ◽  
Vol 42 (7) ◽  
pp. 2181-2200
Author(s):  
Daniela Zöller ◽  
Corrado Sandini ◽  
Marie Schaer ◽  
Stephan Eliez ◽  
Danielle S. Bassett ◽  
...  
Keyword(s):  
Resting State ◽  
Structural Control ◽  
Cost Effective ◽  
Brain Dynamics ◽  
Functional Brain ◽  
Brain States

scholarly journals Dynamic Properties of Simulated Brain Network Models and Empirical Resting State Data

2018 ◽  
Author(s):  
Amrit Kashyap ◽  
Shella Keilholz
Keyword(s):  
Resting State ◽  
Dynamic Properties ◽  
Brain Activity ◽  
Temporal Structure ◽  
Structural Connectivity ◽  
Network Models ◽  
Resting State Fmri ◽  
Brain Network ◽  
Dynamical Analysis ◽  
Whole Brain

AbstractBrain Network Models have become a promising theoretical framework in simulating signals that are representative of whole brain activity such as resting state fMRI. However, it has been difficult to compare the complex brain activity between simulated and empirical data. Previous studies have used simple metrics that surmise coordination between regions such as functional connectivity, and we extend on this by using various different dynamical analysis tools that are currently used to understand resting state fMRI. We show that certain properties correspond to the structural connectivity input that is shared between the models, and certain dynamic properties relate more to the mathematical description of the Brain Network Model. We conclude that the dynamic properties that gauge more temporal structure rather than spatial coordination in the rs-fMRI signal seem to provide the largest contrasts between different BNMs and the unknown empirical dynamical system. Our results will be useful in constraining and developing more realistic simulations of whole brain activity.

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

2018 ◽  
Vol 129 (5) ◽  
pp. 942-958 ◽  
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.

scholarly journals Rude mechanicals in brain haemodynamics: non-neural actors that influence blood flow

2020 ◽  
Vol 376 (1815) ◽  
pp. 20190635
Author(s):  
Aniruddha Das ◽  
Kevin Murphy ◽  
Patrick J. Drew
Keyword(s):  
Resting State ◽  
Vascular Tissue ◽  
Functional Neuroimaging ◽  
Brain Activity ◽  
Blood Oxygenation ◽  
The Body ◽  
Brain Physiology ◽  
Non Invasive ◽  
Local Fluctuations ◽  
Brain States

Fluctuations in blood oxygenation and flow are widely used to infer brain activity during resting-state functional magnetic resonance imaging (fMRI). However, there are strong systemic and vascular contributions to resting-state signals that are unrelated to ongoing neural activity. Importantly, these non-neural contributions to haemodynamic signals (or ‘rude mechanicals’) can be as large as or larger than the neurally evoked components. Here, we review the two broad classes of drivers of these signals. One is systemic and is tied to fluctuations in external drivers such as heart rate and breathing, and the robust autoregulatory mechanisms that try to maintain a constant milieu in the brain. The other class comprises local, active fluctuations that appear to be intrinsic to vascular tissue and are likely similar to active local fluctuations seen in vasculature all over the body. In this review, we describe these non-neural fluctuations and some of the tools developed to correct for them when interpreting fMRI recordings. However, we also emphasize the links between these vascular fluctuations and brain physiology and point to ways in which fMRI measurements can be used to exploit such links to gain valuable information about neurovascular health and about internal brain states. This article is part of the theme issue ‘Key relationships between non-invasive functional neuroimaging and the underlying neuronal activity’.

scholarly journals Latent functional connectivity underlying multiple brain states

2021 ◽  
Author(s):  
Ethan M McCormick ◽  
Katelyn L Arnemann ◽  
Takuya Ito ◽  
Stephen Jose Hanson ◽  
Michael W Cole
Keyword(s):  
Functional Connectivity ◽  
Resting State ◽  
Network Architecture ◽  
Brain Activity ◽  
Brain Network ◽  
Mathematical Framework ◽  
Human Connectome Project ◽  
Brain States ◽  
Task Conditions ◽  
Psychometric G

Functional connectivity (FC) studies have predominantly focused on resting state, where ongoing dynamics are thought to primarily reflect the brain's intrinsic network architecture, which is thought to be broadly relevant to brain function because it persists across brain states. However, it is unknown whether resting state is the optimal state for measuring intrinsic FC. We propose that latent FC, reflecting patterns of connectivity shared across many brain states, may better capture intrinsic FC relative to measures derived from resting state alone. We estimated latent FC in relation to 7 highly distinct task states (24 task conditions) and resting state using fMRI data from 352 participants from the Human Connectome Project. Latent FC was estimated independently for each connection by applying leave-one-task-out factor analysis on the state FC estimates. Compared to resting-state connectivity, we found that latent connectivity improves generalization to held-out brain states, better explaining patterns of both connectivity and task-evoked brain activity. We also found that latent connectivity improved prediction of behavior, measured by the general intelligence factor psychometric g. Our results suggest that patterns of FC shared across many brain states, rather than just resting state, better reflects general, state-independent connectivity. This affirms the notion of "intrinsic" brain network architecture as a set of connectivity properties persistent across brain states, providing an updated conceptual and mathematical framework of intrinsic connectivity as a latent factor.

scholarly journals Inferring multi-scale neural mechanisms with brain network modelling

2017 ◽  
Author(s):  
Michael Schirner ◽  
Anthony Randal McIntosh ◽  
Viktor K. Jirsa ◽  
Gustavo Deco ◽  
Petra Ritter
Keyword(s):  
Resting State ◽  
Brain Activity ◽  
Empirical Studies ◽  
Structural Connectivity ◽  
Resting State Fmri ◽  
Brain Network ◽  
Neural Population ◽  
Network Modelling ◽  
Multi Scale ◽  
Activity Measurements

The neurophysiological processes underlying non-invasive brain activity measurements are not well understood. Here, we developed a novel connectome-based brain network model that integrates individual structural and functional data with neural population dynamics to support multi-scale neurophysiological inference. Simulated populations were linked by structural connectivity and, as a novelty, driven by electroencephalography (EEG) source activity. Simulations not only predicted subjects’ individual resting-state functional magnetic resonance imaging (fMRI) time series and spatial network topologies over 20 minutes of activity, but more importantly, they also revealed precise neurophysiological mechanisms that underlie and link six empirical observations from different scales and modalities: (1) slow resting-state fMRI oscillations, (2) spatial topologies of functional connectivity networks, (3) excitation-inhibition balance, (4, 5) pulsed inhibition on short and long time scales, and (6) fMRI power-law scaling. These findings underscore the potential of this new modelling framework for general inference and integration of neurophysiological knowledge to complement empirical studies.

scholarly journals STXBP1 Syndrome Is Characterized by Inhibition-Dominated Dynamics of Resting-State EEG

2021 ◽  
Vol 12 ◽  
Author(s):  
Simon J. Houtman ◽  
Hanna C. A. Lammertse ◽  
Annemiek A. van Berkel ◽  
Ganna Balagura ◽  
Elena Gardella ◽  
...  
Keyword(s):  
Resting State ◽  
Large Scale ◽  
Brain Activity ◽  
Neurodevelopmental Disorder ◽  
Epileptic Encephalopathy ◽  
Frequency Resolution ◽  
Similar Frequency ◽  
Psychomotor Delay ◽  
Local Circuitry ◽  
The Impact

STXBP1 syndrome is a rare neurodevelopmental disorder caused by heterozygous variants in the STXBP1 gene and is characterized by psychomotor delay, early-onset developmental delay, and epileptic encephalopathy. Pathogenic STXBP1 variants are thought to alter excitation-inhibition (E/I) balance at the synaptic level, which could impact neuronal network dynamics; however, this has not been investigated yet. Here, we present the first EEG study of patients with STXBP1 syndrome to quantify the impact of the synaptic E/I dysregulation on ongoing brain activity. We used high-frequency-resolution analyses of classical and recently developed methods known to be sensitive to E/I balance. EEG was recorded during eyes-open rest in children with STXBP1 syndrome (n = 14) and age-matched typically developing children (n = 50). Brain-wide abnormalities were observed in each of the four resting-state measures assessed here: (i) slowing of activity and increased low-frequency power in the range 1.75–4.63 Hz, (ii) increased long-range temporal correlations in the 11–18 Hz range, (iii) a decrease of our recently introduced measure of functional E/I ratio in a similar frequency range (12–24 Hz), and (iv) a larger exponent of the 1/f-like aperiodic component of the power spectrum. Overall, these findings indicate that large-scale brain activity in STXBP1 syndrome exhibits inhibition-dominated dynamics, which may be compensatory to counteract local circuitry imbalances expected to shift E/I balance toward excitation, as observed in preclinical models. We argue that quantitative EEG investigations in STXBP1 and other neurodevelopmental disorders are a crucial step to understand large-scale functional consequences of synaptic E/I perturbations.

scholarly journals LSD flattens the brain′s energy landscape: evidence from receptor-informed network control theory

2021 ◽  
Author(s):  
S. Parker Singleton ◽  
Andrea I Luppi ◽  
Robin L. Carhart-Harris ◽  
Josephine Cruzat ◽  
Leor Roseman ◽  
...  
Keyword(s):  
Control Theory ◽  
Subjective Experience ◽  
Brain Activity ◽  
Energy Landscape ◽  
Network Control ◽  
State Transitions ◽  
Brain State ◽  
Control Analysis ◽  
Brain States ◽  
The Brain

Psychedelics like lysergic acid diethylamide (LSD) offer a powerful window into the function of the human brain and mind, by temporarily altering subjective experience through their neurochemical effects. The RElaxed Beliefs Under Psychedelics (REBUS) model postulates that 5-HT2a receptor agonism allows the brain to explore its dynamic landscape more readily, as suggested by more diverse (entropic) brain activity. Formally, this effect is theorized to correspond to a reduction in the energy required to transition between different brain-states, i.e. a ″flattening of the energy landscape.″ However, this hypothesis remains thus far untested. Here, we leverage network control theory to map the brain′s energy landscape, by quantifying the energy required to transition between recurrent brain states. In accordance with the REBUS model, we show that LSD reduces the energy required for brain-state transitions, and, furthermore, that this reduction in energy correlates with more frequent state transitions and increased entropy of brain-state dynamics. Through network control analysis that incorporates the spatial distribution of 5-HT2a receptors, we demonstrate the specific role of this receptor in flattening the brain′s energy landscape. Also, in accordance with REBUS, we show that the occupancy of bottom-up states is increased by LSD. In addition to validating fundamental predictions of the REBUS model of psychedelic action, this work highlights the potential of receptor-informed network control theory to provide mechanistic insights into pharmacological modulation of brain dynamics.

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