scholarly journals The connectome spectrum as a canonical basis for a sparse representation of fast brain activity

2021 ◽  
Author(s):  
Joan Rué-Queralt ◽  
Katharina Glomb ◽  
David Pascucci ◽  
Sebastien Tourbier ◽  
Margherita Carboni ◽  
...  
Keyword(s):  
Visual Processing ◽  
Degrees Of Freedom ◽  
Large Scale ◽  
Functional Organization ◽  
Brain Activity ◽  
Structural Connectivity ◽  
Canonical Basis ◽  
Structural Mri ◽  
Brain Dynamics ◽  
Electrical Brain Activity

ABSTRACTThe functional organization of neural processes is constrained by the brain’s intrinsic structural connectivity. Here, we explore the potential of exploiting this structure in order to improve the signal representation properties of brain activity and its dynamics. Using a multi-modal imaging dataset (electroencephalography, structural MRI and diffusion MRI), we represent electrical brain activity at the cortical surface as a time-varying composition of harmonic modes of structural connectivity. The harmonic modes are termed connectome harmonics, and their representation is known as the connectome spectrum of the signal. We found that: first, the brain activity signal is more compactly represented by the connectome spectrum than by the traditional area-based representation; second, the connectome spectrum characterizes fast brain dynamics in terms of signal broadcasting profile, revealing different temporal regimes of integration and segregation that are consistent across participants. And last, the connectome spectrum characterises fast brain dynamics with fewer degrees of freedom than area-based signal representations. Specifically, we show that with the connectome spectrum representation, fewer dimensions are needed to capture the differences between low-level and high-level visual processing, and the topological properties of the signal. In summary, this work provides statistical, functional and topological evidence supporting that by accounting for the brain’s structural connectivity fosters a more comprehensive understanding of large-scale dynamic neural functioning.

scholarly journals Harmonic Brain Modes: A Unifying Framework for Linking Space and Time in Brain Dynamics

2017 ◽  
Vol 24 (3) ◽  
pp. 277-293 ◽  
Author(s):  
Selen Atasoy ◽  
Gustavo Deco ◽  
Morten L. Kringelbach ◽  
Joel Pearson
Keyword(s):  
Neural Activity ◽  
Brain Activity ◽  
Activity Patterns ◽  
Structural Connectivity ◽  
Building Blocks ◽  
Mental States ◽  
Brain Dynamics ◽  
Space And Time ◽  
Wide Range ◽  
The Brain

A fundamental characteristic of spontaneous brain activity is coherent oscillations covering a wide range of frequencies. Interestingly, these temporal oscillations are highly correlated among spatially distributed cortical areas forming structured correlation patterns known as the resting state networks, although the brain is never truly at “rest.” Here, we introduce the concept of harmonic brain modes—fundamental building blocks of complex spatiotemporal patterns of neural activity. We define these elementary harmonic brain modes as harmonic modes of structural connectivity; that is, connectome harmonics, yielding fully synchronous neural activity patterns with different frequency oscillations emerging on and constrained by the particular structure of the brain. Hence, this particular definition implicitly links the hitherto poorly understood dimensions of space and time in brain dynamics and its underlying anatomy. Further we show how harmonic brain modes can explain the relationship between neurophysiological, temporal, and network-level changes in the brain across different mental states ( wakefulness, sleep, anesthesia, psychedelic). Notably, when decoded as activation of connectome harmonics, spatial and temporal characteristics of neural activity naturally emerge from the interplay between excitation and inhibition and this critical relation fits the spatial, temporal, and neurophysiological changes associated with different mental states. Thus, the introduced framework of harmonic brain modes not only establishes a relation between the spatial structure of correlation patterns and temporal oscillations (linking space and time in brain dynamics), but also enables a new dimension of tools for understanding fundamental principles underlying brain dynamics in different states of consciousness.

scholarly journals Loss of consciousness reduces the stability of brain hubs and the heterogeneity of brain dynamics

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Ane López-González ◽  
Rajanikant Panda ◽  
Adrián Ponce-Alvarez ◽  
Gorka Zamora-López ◽  
Anira Escrichs ◽  
...  
Keyword(s):  
Brain Injuries ◽  
Brain Activity ◽  
Structural Connectivity ◽  
Conscious State ◽  
Brain Dynamics ◽  
Disorder Of Consciousness ◽  
Low Level ◽  
States Of Consciousness ◽  
The Stability ◽  
Different Levels

AbstractLow-level states of consciousness are characterized by disruptions of brain activity that sustain arousal and awareness. Yet, how structural, dynamical, local and network brain properties interplay in the different levels of consciousness is unknown. Here, we study fMRI brain dynamics from patients that suffered brain injuries leading to a disorder of consciousness and from healthy subjects undergoing propofol-induced sedation. We show that pathological and pharmacological low-level states of consciousness display less recurrent, less connected and more segregated synchronization patterns than conscious state. We use whole-brain models built upon healthy and injured structural connectivity to interpret these dynamical effects. We found that low-level states of consciousness were associated with reduced network interactions, together with more homogeneous and more structurally constrained local dynamics. Notably, these changes lead the structural hub regions to lose their stability during low-level states of consciousness, thus attenuating the differences between hubs and non-hubs brain dynamics.

scholarly journals Emerging imaging methods to study whole-brain function in rodent models

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Marija Markicevic ◽  
Iurii Savvateev ◽  
Christina Grimm ◽  
Valerio Zerbi
Keyword(s):  
Large Scale ◽  
Functional Organization ◽  
Imaging Techniques ◽  
Brain Activity ◽  
Research Question ◽  
Careful Consideration ◽  
Mammalian Brain ◽  
High Temporal Resolution ◽  
Imaging Methods ◽  
Evoked Activity

AbstractIn the past decade, the idea that single populations of neurons support cognition and behavior has gradually given way to the realization that connectivity matters and that complex behavior results from interactions between remote yet anatomically connected areas that form specialized networks. In parallel, innovation in brain imaging techniques has led to the availability of a broad set of imaging tools to characterize the functional organization of complex networks. However, each of these tools poses significant technical challenges and faces limitations, which require careful consideration of their underlying anatomical, physiological, and physical specificity. In this review, we focus on emerging methods for measuring spontaneous or evoked activity in the brain. We discuss methods that can measure large-scale brain activity (directly or indirectly) with a relatively high temporal resolution, from milliseconds to seconds. We further focus on methods designed for studying the mammalian brain in preclinical models, specifically in mice and rats. This field has seen a great deal of innovation in recent years, facilitated by concomitant innovation in gene-editing techniques and the possibility of more invasive recordings. This review aims to give an overview of currently available preclinical imaging methods and an outlook on future developments. This information is suitable for educational purposes and for assisting scientists in choosing the appropriate method for their own research question.

scholarly journals The Temporal Dynamics of Object Processing in Visual Cortex during the Transition from Distributed to Focused Spatial Attention

2011 ◽  
Vol 23 (12) ◽  
pp. 4094-4105 ◽  
Author(s):  
Chien-Te Wu ◽  
Melissa E. Libertus ◽  
Karen L. Meyerhoff ◽  
Marty G. Woldorff
Keyword(s):  
Visual Cortex ◽  
Spatial Attention ◽  
Cognitive Neuroscience ◽  
Visual Processing ◽  
Object Representation ◽  
Temporal Dynamics ◽  
Brain Activity ◽  
Object Processing ◽  
Electrical Brain Activity

Several major cognitive neuroscience models have posited that focal spatial attention is required to integrate different features of an object to form a coherent perception of it within a complex visual scene. Although many behavioral studies have supported this view, some have suggested that complex perceptual discrimination can be performed even with substantially reduced focal spatial attention, calling into question the complexity of object representation that can be achieved without focused spatial attention. In the present study, we took a cognitive neuroscience approach to this problem by recording cognition-related brain activity both to help resolve the questions about the role of focal spatial attention in object categorization processes and to investigate the underlying neural mechanisms, focusing particularly on the temporal cascade of these attentional and perceptual processes in visual cortex. More specifically, we recorded electrical brain activity in humans engaged in a specially designed cued visual search paradigm to probe the object-related visual processing before and during the transition from distributed to focal spatial attention. The onset times of the color popout cueing information, indicating where within an object array the subject was to shift attention, was parametrically varied relative to the presentation of the array (i.e., either occurring simultaneously or being delayed by 50 or 100 msec). The electrophysiological results demonstrate that some levels of object-specific representation can be formed in parallel for multiple items across the visual field under spatially distributed attention, before focal spatial attention is allocated to any of them. The object discrimination process appears to be subsequently amplified as soon as focal spatial attention is directed to a specific location and object. This set of novel neurophysiological findings thus provides important new insights on fundamental issues that have been long-debated in cognitive neuroscience concerning both object-related processing and the role of attention.

scholarly journals Monolingual and bilingual infants rely on the same brain networks: Evidence from resting-state functional connectivity

2020 ◽  
Author(s):  
Borja Blanco ◽  
Monika Molnar ◽  
Manuel Carreiras ◽  
Liam H. Collins-Jones ◽  
Ernesto Vidal ◽  
...  
Keyword(s):  
Human Brain ◽  
Large Scale ◽  
Near Infrared ◽  
Functional Organization ◽  
Brain Activity ◽  
Functional Brain Imaging ◽  
Resting State Functional Connectivity ◽  
Functional Near Infrared Spectroscopy ◽  
Behavioural Research ◽  
Infant Brain

AbstractThis study examines whether bilingual exposure has a profound effect on the functional organization of the developing human brain during infancy. Recent behavioural research attests that monolingual vs. bilingual experience affects cognitive and linguistic processes already during the first months of life. However, to what extent the intrinsic organization of the infant human brain adapts to monolingual vs. bilingual environments is unclear. We measured spontaneous hemodynamic brain activity using functional near-infrared spectroscopy (fNIRS) in a large cohort (N=99) of 4-month-old monolingual and bilingual infants. We implemented well-established analysis approaches of functional brain imaging that enabled us to reveal the functional organization of the infant brain in large-scale cortical networks, and to perform group-level comparisons (i.e., monolingual vs. bilingual groups) in a reliable manner. Our results revealed no differences between the intrinsic functional organization of the developing monolingual and bilingual infant brain at 4 months of age.

scholarly journals Segregation, integration, and balance of large-scale resting brain networks configure different cognitive abilities

2021 ◽  
Vol 118 (23) ◽  
pp. e2022288118
Author(s):  
Rong Wang ◽  
Mianxin Liu ◽  
Xinhong Cheng ◽  
Ying Wu ◽  
Andrea Hildebrandt ◽  
...  
Keyword(s):  
Cognitive Abilities ◽  
Structural Equation ◽  
Large Scale ◽  
Functional Organization ◽  
Diffusion Tensor ◽  
Brain Activity ◽  
Brain Networks ◽  
Deep Understanding ◽  
Human Cognition ◽  
Equation Modeling

Diverse cognitive processes set different demands on locally segregated and globally integrated brain activity. However, it remains an open question how resting brains configure their functional organization to balance the demands on network segregation and integration to best serve cognition. Here we use an eigenmode-based approach to identify hierarchical modules in functional brain networks and quantify the functional balance between network segregation and integration. In a large sample of healthy young adults (n = 991), we combine the whole-brain resting state functional magnetic resonance imaging (fMRI) data with a mean-filed model on the structural network derived from diffusion tensor imaging and demonstrate that resting brain networks are on average close to a balanced state. This state allows for a balanced time dwelling at segregated and integrated configurations and highly flexible switching between them. Furthermore, we employ structural equation modeling to estimate general and domain-specific cognitive phenotypes from nine tasks and demonstrate that network segregation, integration, and their balance in resting brains predict individual differences in diverse cognitive phenotypes. More specifically, stronger integration is associated with better general cognitive ability, stronger segregation fosters crystallized intelligence and processing speed, and an individual’s tendency toward balance supports better memory. Our findings provide a comprehensive and deep understanding of the brain’s functioning principles in supporting diverse functional demands and cognitive abilities and advance modern network neuroscience theories of human cognition.

Integrative Visuomotor Behavior Is Associated With Interregionally Coherent Oscillations in the Human Brain

1998 ◽  
Vol 79 (3) ◽  
pp. 1567-1573 ◽  
Author(s):  
Joseph Classen ◽  
Christian Gerloff ◽  
Manabu Honda ◽  
Mark Hallett
Keyword(s):  
Motor Cortex ◽  
Human Brain ◽  
Large Scale ◽  
Isometric Force ◽  
Brain Activity ◽  
Motor Task ◽  
Low Frequency ◽  
Visuomotor Behavior ◽  
Electrical Brain Activity ◽  
Eeg Recordings

Classen, Joseph, Christian Gerloff, Manabu Honda, and Mark Hallett. Integrative visuomotor behavior is associated with interregionally coherent oscillations in the human brain. J. Neurophysiol. 79: 1567–1573, 1998. Coherent electrical brain activity has been demonstrated to be associated with perceptual events in mammals. It is unclear whether or not it is also a mechanism instrumental in the performance of sensorimotor tasks requiring the continuous processing of information between primarily executive and receptive brain areas. In particular it is unknown whether or not interregional coherent activity detectable in electroencephalographic (EEG) recordings on the scalp reflects interareal functional cooperativity in humans. We studied patterns of changes in EEG-coherence associated with a visuomotor force-tracking task in seven subjects. Interregional coherence of EEG signals recorded from scalp regions overlying the visual and the motor cortex increased in comparison to a resting condition when subjects tracked a visual target by producing an isometric force with their right index finger. Coherence between visual and motor cortex decreased when the subjects produced a similar motor output in the presence of a visual distractor and was unchanged in a purely visual and purely motor task. Increases and decreases of coherence were best differentiated in the low beta frequency range (13–21 Hz). This observation suggests a special functional significance of low frequency oscillations in information processing in large-scale networks. These findings substantiate the view that coherent brain activity underlies integrative sensorimotor behavior.

scholarly journals Structural differences between healthy subjects and patients with schizophrenia and schizoaffective disorder: A graph and control theoretical perspective

2021 ◽  
Author(s):  
Cristiana Dimulescu ◽  
Serdar Gareayaghi ◽  
Fabian Kamp ◽  
Sophie Fromm ◽  
Klaus Obermayer ◽  
...  
Keyword(s):  
Schizoaffective Disorder ◽  
Large Scale ◽  
Brain Activity ◽  
Theoretical Perspective ◽  
Structural Mri ◽  
Input Energy ◽  
Strong Reduction ◽  
Patient Groups ◽  
And Control ◽  
The Brain

AbstractThe coordinated, dynamical interactions of large-scale networks give rise to cognitive function. Recent advances in network neuroscience have suggested that the anatomical organization of such networks puts a fundamental constraint on the dynamical landscape of the brain. Consequently, changes in large-scale brain activity have been hypothesized to underlie many neurological and psychiatric disorders. Specifically, evidence has emerged that large-scale dysconnectivity might play a crucial role in the pathophysiology of schizophrenia. To investigate potential differences in graph and control theoretic measures between patients with schizophrenia (SCZ), patients with schizoaffective disorder (SCZaff) and matched healthy controls (HC), we use structural MRI data. More specifically, we first calculate seven graph measures of integration, segregation, centrality and resilience and test for group differences. Second, we extend our analysis beyond these traditional measures and employ a simplified noise-free linear discrete-time and time-invariant network model to calculate two complementary measures of controllability. Average controllability identifies brain areas that can guide brain activity into different, easily reachable states with little input energy. Modal controllability on the other hand, characterizes regions that can push the brain into difficult-to-reach states, i.e. states that require substantial input energy. We identified differences in standard network and controllability measures for both patient groups compared to HCs. Specifically, we found a strong reduction of betweenness centrality for both patient groups and a strong reduction in average controllability for the SCZ group again in comparison to the HC group. Our findings of network level deficits might help to explain the many cognitive deficits associated with these disorders.

scholarly journals Harmonic brain modes: a unifying framework for linking space and time in brain dynamics

2017 ◽  
Author(s):  
Selen Atasoy ◽  
Gustavo Deco ◽  
Morten L. Kringelbach ◽  
Joel Pearson
Keyword(s):  
Neural Activity ◽  
Brain Activity ◽  
Activity Patterns ◽  
Structural Connectivity ◽  
Building Blocks ◽  
Mental States ◽  
Brain Dynamics ◽  
Space And Time ◽  
Wide Range ◽  
The Brain

AbstractA fundamental characteristic of spontaneous brain activity is coherent oscillations covering a wide range of frequencies. Interestingly, these temporal oscillations are highly correlated among spatially distributed cortical areas forming structured correlation patterns known as the resting state networks, although the brain is never truly at ‘rest’. Here, we introduce the concept of “harmonic brain modes” – fundamental building blocks of complex spatiotemporal patterns of neural activity. We define these elementary harmonic brain modes as harmonic modes of structural connectivity; i.e. connectome harmonics, yielding fully synchronous neural activity patterns with different frequency oscillations emerging on and constrained by the particular structure of the brain. Hence, this particular definition implicitly links the hitherto poorly understood dimensions of space and time in brain dynamics and its underlying anatomy. Further we show how harmonic brain modes can explain the relationship between neurophysiological, temporal and network-level changes in the brain across different mental states; (wakefulness, sleep, anaesthesia, psychedelic). Notably, when decoded as activation of connectome harmonics, spatial and temporal characteristics of neural activity naturally emerge from the interplay between excitation and inhibition and this critical relation fits the spatial, temporal and neurophysiological changes associated with different mental states. Thus, the introduced framework of harmonic brain modes not only establishes a relation between the spatial structure of correlation patterns and temporal oscillations (linking space and time in brain dynamics), but also enables a new dimension of tools for understanding fundamental principles underlying brain dynamics in different states of consciousness.

scholarly journals On the edge of criticality: strength-dependent perturbation unveils delicate balance between fluctuation and oscillation in brain dynamics

2021 ◽  
Author(s):  
Yontatan Sanz Perl ◽  
Anira Escrichs ◽  
Enzo Tagliazucchi ◽  
Morten L Kringelbach ◽  
Gustavo Deco
Keyword(s):  
Human Brain ◽  
Brain Function ◽  
Large Scale ◽  
Brain Activity ◽  
Brain Dynamics ◽  
Functional Networks ◽  
Oscillatory Regime ◽  
Delicate Balance ◽  
Local Strength ◽  
Global Strength

Going beyond previous research, we use strength-dependent perturbation to obtain a deeper understanding of the mechanisms underlying the emergence of large-scale brain activity. Despite decades of research, we still have a shallow understanding of the role and generating mechanisms of the ubiquitous fluctuations and oscillations found in recordings of brain dynamics. Here, we used global strength-dependent perturbation to give a causal mechanistic description of human brain function providing a delicate balance between fluctuation and oscillation on the edge of criticality. After application of precise local strength-dependent perturbations and measuring the well-known perturbative complexity index, we demonstrated that the overall balance is shifted towards a fluctuating regime which is superior in terms of enhancing different functional networks compared to the oscillatory regime. This framework can generate specific, testable empirical predictions to be tested in human stimulation studies with strength-dependent rather than constant perturbation. Overall, our novel strength-dependent perturbation framework demonstrates that the human brain is poised on the edge of criticality, between fluctuations to oscillations, allowing for maximal flexibility.

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