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 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.

The brain represents people as the mental states they habitually experience

2018 ◽  
Author(s):  
Mark Allen Thornton ◽  
Miriam E. Weaverdyck ◽  
Diana Tamir
Keyword(s):  
Social Life ◽  
Functional Neuroimaging ◽  
Brain Activity ◽  
Activity Patterns ◽  
Reaction Times ◽  
Mental States ◽  
Weighted Sums ◽  
Famous Person ◽  
Binary Choices ◽  
The Brain

Social life requires us to treat each person according to their unique disposition: habitually enthusiastic friends need occasional grounding, whereas pessimistic colleagues require cheering-up. To tailor our behavior to specific individuals, we must represent their idiosyncrasies. Here we advance a hypothesis about how the brain achieves this goal: our representations of other people reflect the mental states we perceive those people to habitually experience. That is, rather than representing other people via traits, our brains represent people as the sums of their states. For example, if a perceiver observes that another person is frequently cheerful, sometimes thoughtful, and rarely grumpy, the perceiver’s representation of that person will be comprised of their representations of the mental states cheerfulness, thoughtfulness, and grumpiness, combined in a corresponding ratio. We tested this hypothesis by measuring whether neural representations of people could be accurately reconstructed by summing state representations. Separate participants underwent functional neuroimaging while considering famous individuals and individual mental states. Online participants rated how often each famous person experiences each state. Results supported the summed state hypothesis: frequency-weighted sums of state-specific brain activity patterns accurately reconstructed person-specific patterns. Moreover, the summed state account outperformed the established alternative – that people represent others using trait dimensions – in explaining interpersonal similarity, as measured through neural patterns, explicit ratings, binary choices, reaction times, and the semantics of biographical text. Together these findings demonstrate that the brain represents other people as the sums of the mental states they are perceived to experience.

scholarly journals Modelling the impact of structural directionality on connectome-based models of neural activity

2020 ◽  
Vol 8 (4) ◽  
Author(s):  
Amelia Padmore ◽  
Martin R Nelson ◽  
Nadia Chuzhanova ◽  
Jonathan J Crofts
Keyword(s):  
Neural Activity ◽  
Activity Patterns ◽  
Structural Connectivity ◽  
Directed Network ◽  
Fixed Point Attractor ◽  
Important Challenge ◽  
Brain States ◽  
Chemical Synapses ◽  
The Impact ◽  
The Brain

Abstract Understanding structure--function relationships in the brain remains an important challenge in neuroscience. However, whilst structural brain networks are intrinsically directed, due to the prevalence of chemical synapses in the cortex, most studies in network neuroscience represent the brain as an undirected network. Here, we explore the role that directionality plays in shaping transition dynamics of functional brain states. Using a system of Hopfield neural elements with heterogeneous structural connectivity given by different species and parcellations (cat, Caenorhabditis elegans and two macaque networks), we investigate the effect of removing directionality of connections on brain capacity, which we quantify via its ability to store attractor states. In addition to determining large numbers of fixed-point attractor sets, we deploy the recently developed basin stability technique in order to assess the global stability of such brain states, which can be considered a proxy for network state robustness. Our study indicates that not only can directed network topology have a significant effect on the information capacity of connectome-based networks, but it can also impact significantly the domains of attraction of the aforementioned brain states. In particular, we find network modularity to be a key mechanism underlying the formation of neural activity patterns, and moreover, our results suggest that neglecting network directionality has the scope to eliminate states that correlate highly with the directed modular structure of the brain. A numerical analysis of the distribution of attractor states identified a small set of prototypical direction-dependent activity patterns that potentially constitute a `skeleton' of the non-stationary dynamics typically observed in the brain. This study thereby emphasizes the substantial role network directionality can have in shaping the brain's ability to both store and process information.

scholarly journals Determination of Dynamic Brain Connectivity via Spectral Analysis

2021 ◽  
Vol 15 ◽  
Author(s):  
Peter A. Robinson ◽  
James A. Henderson ◽  
Natasha C. Gabay ◽  
Kevin M. Aquino ◽  
Tara Babaie-Janvier ◽  
...  
Keyword(s):  
Spectral Analysis ◽  
Brain Structure ◽  
Brain Connectivity ◽  
Spatial Scales ◽  
Effective Connectivity ◽  
Physical Nature ◽  
Building Blocks ◽  
Brain Dynamics ◽  
Compact Representation ◽  
The Brain

Spectral analysis based on neural field theory is used to analyze dynamic connectivity via methods based on the physical eigenmodes that are the building blocks of brain dynamics. These approaches integrate over space instead of averaging over time and thereby greatly reduce or remove the temporal averaging effects, windowing artifacts, and noise at fine spatial scales that have bedeviled the analysis of dynamical functional connectivity (FC). The dependences of FC on dynamics at various timescales, and on windowing, are clarified and the results are demonstrated on simple test cases, demonstrating how modes provide directly interpretable insights that can be related to brain structure and function. It is shown that FC is dynamic even when the brain structure and effective connectivity are fixed, and that the observed patterns of FC are dominated by relatively few eigenmodes. Common artifacts introduced by statistical analyses that do not incorporate the physical nature of the brain are discussed and it is shown that these are avoided by spectral analysis using eigenmodes. Unlike most published artificially discretized “resting state networks” and other statistically-derived patterns, eigenmodes overlap, with every mode extending across the whole brain and every region participating in every mode—just like the vibrations that give rise to notes of a musical instrument. Despite this, modes are independent and do not interact in the linear limit. It is argued that for many purposes the intrinsic limitations of covariance-based FC instead favor the alternative of tracking eigenmode coefficients vs. time, which provide a compact representation that is directly related to biophysical brain dynamics.

Recognition of true and false states of the brain by means of the electroencephalogram wavelet analysis

2021 ◽  
Author(s):  
S.S. Pertsov ◽  
E.A. Yumatov ◽  
N.A. Karatygin ◽  
E.N. Dudnik ◽  
A.E. Khramov ◽  
...  
Keyword(s):  
Wavelet Transform ◽  
Experimental Model ◽  
Continuous Wavelet Transform ◽  
Brain Activity ◽  
Mental States ◽  
Mental Activity ◽  
Continuous Wavelet ◽  
Wavelet Energy ◽  
True State ◽  
The Brain

It is a well-known fact that mental activity of the brain can be presented by two different states, i.e., the true state and the false state. A promising method of the electroencephalogram (EEG) wavelet transform has been developed over recent years. Using this method, we evaluated the principle possibility for direct objective registration of mental activity in the human brain. Previously we developed and described (published) a new experimental model and software for recognizing the true and false mental responses of a person with the EEG wavelet transform. The developed experimental model and software-and-data support allowed us to compare (by EEG parameters) two mental states of brain activity, one of which is the false state, while another is the true state. The goal of this study is to develop an absolutely new information technology for recognizing the true and false states in mental activity of the brain by means of the EEG wavelet transform. Our study showed that the true and false states of the brain can be distinguished using the method of continuous wavelet transform and calculation of the EEG wavelet energy. It was revealed that the main differences between truthful and false mental responses are observed in the delta and alpha ranges of the EEG. In the EEG delta rhythm, the wavelet energy is much higher under conditions of the false response as compared to that in the true response. In the EEG alpha rhythm, the wavelet energy is significantly higher with the true answer than in the false one. These data open a new principal possibility of revealing the true and false mental state of the brain by means of continuous wavelet transform and calculation of the EEG wavelet energy.

Mind, identity theory of

2018 ◽  
Author(s):  
Frank Jackson
Keyword(s):  
Central Nervous System ◽  
Nervous System ◽  
Identity Theory ◽  
Brain Activity ◽  
Modern Science ◽  
Mental States ◽  
Ontological Categories ◽  
Great Merit ◽  
The Mind ◽  
The Brain

We know that the brain is intimately connected with mental activity. Indeed, doctors now define death in terms of the cessation of the relevant brain activity. The identity theory of mind holds that the intimate connection is identity: the mind is the brain, or, more precisely, mental states are states of the brain. The theory goes directly against a long tradition according to which mental and material belong to quite distinct ontological categories – the mental being essentially conscious, the material essentially unconscious. This tradition has been bedevilled by the problem of how essentially immaterial states could be caused by the material world, as would happen when we see a tree, and how they could cause material states, as would happen when we decide to make an omelette. A great merit of the identity theory is that it avoids this problem: interaction between mental and material becomes simply interaction between one subset of material states, namely certain states of a sophisticated central nervous system, and other material states. The theory also brings the mind within the scope of modern science. More and more phenomena are turning out to be explicable in the physical terms of modern science: phenomena once explained in terms of spells, possession by devils, Thor’s thunderbolts, and so on, are now explained in more mundane, physical terms. If the identity theory is right, the same goes for the mind. Neuroscience will in time reveal the secrets of the mind in the same general way that the theory of electricity reveals the secrets of lightning. This possibility has received enormous support from advances in computing. We now have at least the glimmerings of an idea of how a purely material or physical system could do some of the things minds can do. Nevertheless, there are many questions to be asked of the identity theory. How could states that seem so different turn out to be one and the same? Would neurophysiologists actually see my thoughts and feelings if they looked at my brain? When we report on our mental states what are we reporting on – our brains?

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 On the methods for reactivation and replay analysis

2020 ◽  
Vol 375 (1799) ◽  
pp. 20190231 ◽  
Author(s):  
David Tingley ◽  
Adrien Peyrache
Keyword(s):  
Neural Activity ◽  
Temporal Structure ◽  
Activity Patterns ◽  
Large Body ◽  
Neural Data ◽  
History Of ◽  
Methodological Approaches ◽  
Neuronal Patterns ◽  
External Stimuli ◽  
The Brain

A major task in the history of neurophysiology has been to relate patterns of neural activity to ongoing external stimuli. More recently, this approach has branched out to relating current neural activity patterns to external stimuli or experiences that occurred in the past or future. Here, we aim to review the large body of methodological approaches used towards this goal, and to assess the assumptions each makes with reference to the statistics of neural data that are commonly observed. These methods primarily fall into two categories, those that quantify zero-lag relationships without examining temporal evolution, termed reactivation , and those that quantify the temporal structure of changing activity patterns, termed replay . However, no two studies use the exact same approach, which prevents an unbiased comparison between findings. These observations should instead be validated by multiple and, if possible, previously established tests. This will help the community to speak a common language and will eventually provide tools to study, more generally, the organization of neuronal patterns in the brain. This article is part of the Theo Murphy meeting issue ‘Memory reactivation: replaying events past, present and future’.

scholarly journals Clock time: a foreign measure to brain dynamics

2021 ◽  
Author(s):  
Sander van Bree ◽  
María Melcón ◽  
Luca Kolibius ◽  
Casper Kérren ◽  
Maria Wimber ◽  
...  
Keyword(s):  
Brain Activity ◽  
Brain Dynamics ◽  
Neural Firing ◽  
Clock Time ◽  
Time Warping ◽  
Human Thought ◽  
Abrupt Changes ◽  
Information Patterns ◽  
Speed Up ◽  
The Brain

Human thought is highly flexible and dynamic, achieved by evolving patterns of brain activity across groups of cells. Neuroscience aims to understand cognition in the brain by analysing these intricate patterns. Here, we argue that this goal is impeded by the time format of our data – clock time. The brain is a system with its own dynamics and regime of time, with no intrinsic concern for the human-invented second. A more appropriate time format is cycles of brain oscillations, which coordinate neural firing and are widely implicated in cognition. These brain dynamics do not obey clock time – they start out of tune with clock time and drift apart even further as oscillations unpredictably slow down, speed up, and undergo abrupt changes. Since oscillations clock cognition, their dynamics should critically inform our analysis. We describe brain time warping as a new method to transform data in accordance with brain dynamics, which sets the time axis to cycles of clocking oscillations (a native unit) rather than milliseconds (a foreign unit). We also introduce the Brain Time Toolbox, a software library that implements brain time warping for electrophysiology data and tests whether it reveals information patterns in line with how the brain uses them.

scholarly journals Learning multiple variable-speed sequences in striatum via cortical tutoring

eLife ◽  
2017 ◽  
Vol 6 ◽  
Author(s):  
James M Murray ◽  
G Sean Escola
Keyword(s):  
Neural Activity ◽  
Activity Patterns ◽  
Pattern Generation ◽  
Cortical Circuits ◽  
Motor Sequence ◽  
Learned Behaviors ◽  
Multiple Variable ◽  
Lesion Studies ◽  
Inhibitory Circuit ◽  
The Brain

Sparse, sequential patterns of neural activity have been observed in numerous brain areas during timekeeping and motor sequence tasks. Inspired by such observations, we construct a model of the striatum, an all-inhibitory circuit where sequential activity patterns are prominent, addressing the following key challenges: (i) obtaining control over temporal rescaling of the sequence speed, with the ability to generalize to new speeds; (ii) facilitating flexible expression of distinct sequences via selective activation, concatenation, and recycling of specific subsequences; and (iii) enabling the biologically plausible learning of sequences, consistent with the decoupling of learning and execution suggested by lesion studies showing that cortical circuits are necessary for learning, but that subcortical circuits are sufficient to drive learned behaviors. The same mechanisms that we describe can also be applied to circuits with both excitatory and inhibitory populations, and hence may underlie general features of sequential neural activity pattern generation in the brain.

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