Multiscale brain modelling

A central difficulty of brain modelling is to span the range of spatio-temporal scales from synapses to the whole brain. This paper overviews results from a recent model of the generation of brain electrical activity that incorporates both basic microscopic neurophysiology and large-scale brain anatomy to predict brain electrical activity at scales from a few tenths of a millimetre to the whole brain. This model incorporates synaptic and dendritic dynamics, nonlinearity of the firing response, axonal propagation and corticocortical and corticothalamic pathways. Its relatively few parameters measure quantities such as synaptic strengths, corticothalamic delays, synaptic and dendritic time constants, and axonal ranges, and are all constrained by independent physiological measurements. It reproduces quantitative forms of electroencephalograms seen in various states of arousal, evoked response potentials, coherence functions, seizure dynamics and other phenomena. Fitting model predictions to experimental data enables underlying physiological parameters to be inferred, giving a new non-invasive window into brain function that complements slower, but finer-resolution, techniques such as fMRI. Because the parameters measure physiological quantities relating to multiple scales, and probe deep structures such as the thalamus, this will permit the testing of a range of hypotheses about vigilance, cognition, drug action and brain function. In addition, referencing to a standardized database of subjects adds strength and specificity to characterizations obtained.

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Brain Electrical Activity Mapping (BEAM): Computerized Access to Complex Brain Function

International Journal of Neuroscience ◽

10.3109/00207458108991795 ◽

1981 ◽

Vol 13 (1) ◽

pp. 55-65 ◽

Cited By ~ 25

Author(s):

Frank H. Duffy

Keyword(s):

Electrical Activity ◽

Brain Function ◽

Brain Electrical Activity ◽

Brain Electrical Activity Mapping

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Spatio-temporal distribution of brain electrical activity patterns in epilepsy: inputs for cellular neural networks

ISCAS 2001. The 2001 IEEE International Symposium on Circuits and Systems (Cat. No.01CH37196) ◽

10.1109/iscas.2001.921314 ◽

2002 ◽

Cited By ~ 2

Author(s):

R. Tetzlaff ◽

R. Kunz

Keyword(s):

Neural Networks ◽

Electrical Activity ◽

Activity Patterns ◽

Temporal Distribution ◽

Cellular Neural Networks ◽

Brain Electrical Activity ◽

Spatio Temporal

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Meditation-induced effects on whole-brain structural and effective connectivity

10.1101/2021.06.10.447903 ◽

2021 ◽

Author(s):

Eleonora De Filippi ◽

Anira Escrichs ◽

Matthieu Gilson ◽

Marti Sanchez-Fibla ◽

Estela Camara ◽

...

Keyword(s):

High Performance ◽

Large Scale ◽

Effective Connectivity ◽

Temporal Structure ◽

Functional Domain ◽

Anatomical Connectivity ◽

Whole Brain ◽

Spatio Temporal ◽

Large Scale Networks ◽

And Function

In the past decades, there has been a growing scientific interest in characterizing neural correlates of meditation training. Nonetheless, the mechanisms underlying meditation remain elusive. In the present work, we investigated meditation-related changes in structural and functional connectivities (SC and FC, respectively). For this purpose, we scanned experienced meditators and control (naive) subjects using magnetic resonance imaging (MRI) to acquire structural and functional data during two conditions, resting-state and meditation (focused attention on breathing). In this way, we aimed to characterize and distinguish both short-term and long-term modifications in the brain's structure and function. First, we performed a network-based analysis of anatomical connectivity. Then, to analyze the fMRI data, we calculated whole-brain effective connectivity (EC) estimates, relying on a dynamical network model to replicate BOLD signals' spatio-temporal structure, akin to FC with lagged correlations. We compared the estimated EC, FC, and SC links as features to train classifiers to predict behavioral conditions and group identity. The whole-brain SC analysis revealed strengthened anatomical connectivity across large-scale networks for meditators compared to controls. We found that differences in SC were reflected in the functional domain as well. We demonstrated through a machine-learning approach that EC features were more informative than FC and SC solely. Using EC features we reached high performance for the condition-based classification within each group and moderately high accuracies when comparing the two groups in each condition. Moreover, we showed that the most informative EC links that discriminated between meditators and controls involved the same large-scale networks previously found to have increased anatomical connectivity. Overall, the results of our whole-brain model-based approach revealed a mechanism underlying meditation by providing causal relationships at the structure-function level.

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Turbulent-like dynamics in the human brain

10.1101/865923 ◽

2019 ◽

Author(s):

Gustavo Deco ◽

Morten L. Kringelbach

Keyword(s):

Human Brain ◽

Brain Function ◽

Information Transfer ◽

Large Scale ◽

Brain Dynamics ◽

Whole Brain ◽

Large Scale Network ◽

Maximal Sensitivity ◽

Scale Network ◽

Best Fit

SummaryTurbulence facilitates fast energy/information transfer across scales in physical systems. These qualities are important for brain function, but it is currently unknown if the dynamic intrinsic backbone of brain also exhibits turbulence. Using large-scale neuroimaging empirical data from 1003 healthy participants, we demonstrate Kuramoto’s amplitude turbulence in human brain dynamics. Furthermore, we build a whole-brain model with coupled oscillators to demonstrate that the best fit to the data corresponds to a region of maximally developed amplitude turbulence, which also corresponds to maximal sensitivity to the processing of external stimulations (information capability). The model shows the economy of anatomy by following the Exponential Distance Rule of anatomical connections as a cost-of-wiring principle. This establishes a firm link between turbulence and optimal brain function. Overall, our results reveal a way of analysing and modelling whole-brain dynamics that suggests turbulence as the dynamic intrinsic backbone facilitating large scale network communication.

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Meditation-Induced Effects on Whole-Brain Structural and Effective Connectivity

10.21203/rs.3.rs-637392/v1 ◽

2021 ◽

Author(s):

Eleonora De Filippi ◽

Anira Escrichs ◽

Estela Càmara ◽

César Garrido ◽

Martí Sánchez-Fibla ◽

...

Keyword(s):

Large Scale ◽

Effective Connectivity ◽

Temporal Structure ◽

Functional Domain ◽

Anatomical Connectivity ◽

Whole Brain ◽

Spatio Temporal ◽

Magnetic Resonance Imaging Mri ◽

Large Scale Networks ◽

And Function

Abstract In the past decades, there has been a growing scientific interest in characterizing neural correlates of meditation training. Nonetheless, the mechanisms underlying meditation remain elusive. In the present work, we investigated meditation-related changes in structural and functional connectivities (SC and FC, respectively). For this purpose, we scanned experienced meditators and control (naive) subjects using magnetic resonance imaging (MRI) to acquire structural and functional data during two conditions, resting-state and meditation (focused attention on breathing). In this way, we aimed to characterize and distinguish both short-term and long-term modifications in the brain's structure and function. First, we performed a network-based analysis of anatomical connectivity. Then, to analyze the fMRI data, we calculated whole-brain effective connectivity (EC) estimates, relying on a dynamical network model to replicate BOLD signals' spatio-temporal structure, akin to FC with lagged correlations. We compared the estimated EC, FC, and SC links as features to train classifiers to predict behavioral conditions and group identity. The whole-brain SC analysis revealed strengthened anatomical connectivity across large-scale networks for meditators compared to controls. We found that differences in SC were reflected in the functional domain as well. We demonstrated through a machine-learning approach that EC features were more informative than FC and SC solely. Moreover, we showed that the most informative EC links that discriminated between meditators and controls involved the same large-scale networks previously found to have increased anatomical connectivity. Overall, the results of our whole-brain model-based approach revealed a mechanism underlying meditation by providing causal relationships at the structure-function level.

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Neinvazinių galvos smegenų elektrinio aktyvumo tyrimų metodų ir žvilgsnio sekimo sistemos taikymo galimybės reklamos efektyvumo vertinimui

Management Theory and Studies for Rural Business and Infrastructure Development ◽

10.15544/mts.2015.19 ◽

2015 ◽

Vol 37 (2) ◽

pp. 212-220 ◽

Cited By ~ 1

Author(s):

Viktorija Grigaliūnaitė ◽

Lina Pilelienė

Keyword(s):

Eye Tracking ◽

Electrical Activity ◽

Research Methods ◽

Research Result ◽

Marketing Research ◽

Advertising Effectiveness ◽

Brain Electrical Activity ◽

Non Invasive ◽

Analysis And Synthesis ◽

Marketing Activities

The application of the non-invasive brain electrical activity and eye-tracking research methods for the assessment of advertising effectiveness are being analyzed in the article. Traditional marketing research methods are not always sufficient for the complete assessment of advertising effectiveness, thus neuromarketing research methods are often applied to clarify it. Nevertheless, due to the absence of the methodologies of neuromarketing research methods application for evaluating marketing activities, it is important to reveal the possibilities of applying the specific methods for the assessment of advertising effectiveness. The aim of this article is to determine the possibilities of applying non-invasive brain electrical activity and eye-tracking research methods for the evaluation of advertising effectiveness. While achieving the aim of the article, the logical analysis and synthesis of the scientific literature is applied. As a research result, the guidelines for the selection of non-invasive brain electrical activity and eye-tracking research methods for the assessment of advertising effectiveness are formed. Latter guidelines are relevant for the organizations performing neuromarketing researches in both academic and business levels.

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Cross-Modality Coherent Registration of Whole Mouse Brains

10.21203/rs.3.rs-321118/v1 ◽

2021 ◽

Author(s):

Hanchuan Peng ◽

Lei Qu ◽

Yuanyuan Li ◽

Peng Xie ◽

Lijuan Liu ◽

...

Keyword(s):

Large Scale ◽

Single Neuron ◽

Cell Types ◽

Brain Anatomy ◽

Neuron Morphology ◽

Brain Images ◽

Whole Brain ◽

Preparation Methods ◽

Registration Method ◽

3D Images

Abstract Recent whole brain mapping projects are collecting large-scale 3D images using powerful and informative modalities, such as STPT, fMOST, VISoR, or MRI. Registration of these multi-dimensional whole-brain images onto a standard atlas is essential for characterizing neuron types and constructing brain wiring diagrams. However, cross-modality image registration is challenging due to intrinsic variations of brain anatomy and artifacts resulted from different sample preparation methods and imaging modalities. We introduced a cross-modality registration method, called mBrainAligner, which uses coherent landmark mapping as well as deep neural networks to align whole mouse brain images to the standard Allen Common Coordinate Framework atlas. We also built a single cell resolution atlas using the fMOST modality, and used our method to generate whole brain map of 3D full single neuron morphology and neuron cell types.

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Методика пространственно-временного анализа электрической активности головного мозга

Письма в журнал технической физики ◽

10.21883/pjtf.2020.11.49498.18023 ◽

2020 ◽

Vol 46 (11) ◽

pp. 39

Author(s):

А.Е. Руннова ◽

М.О. Журавлев ◽

А.Р. Киселёв ◽

А.О. Сельский

Keyword(s):

Wavelet Transform ◽

Electrical Activity ◽

Continuous Wavelet Transform ◽

Temporal Dynamics ◽

Brain Activity ◽

Activity Patterns ◽

Spatial Dynamics ◽

Continuous Wavelet ◽

Brain Electrical Activity ◽

Spatio Temporal

In the framework of this work a new method based on continuous wavelet transform was proposed for analyzing the spatio-temporal dynamics of brain activity patterns. We described the example of this method application for the analysis of brain electrical activity signals. It is shown that this method has the ability to visually detect the occurrence and spatial dynamics of frequency patterns.

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