Personalization of hybrid brain models from neuroimaging and electrophysiology data

AbstractPersonalization is rapidly becoming standard practice in medical diagnosis and treatment. This study is part of an ambitious program towards computational personalization of neuromodulatory interventions in neuropsychiatry. We propose to model the individual human brain as a network of neural masses embedded in a realistic physical matrix capable of representing measurable electrical brain activity. We call this a hybrid brain model (HBM) to highlight that it encodes both biophysical and physiological characteristics of an individual brain. Although the framework is general, we provide here a pipeline for the integration of anatomical, structural and functional connectivity data obtained from magnetic resonance imaging (MRI), diffuse tensor imaging (DTI connectome) and electroencephalography (EEG). We personalize model parameters through a comparison of simulated cortical functional connectivity with functional connectivity profiles derived from cortically-mapped, subject-specific EEG. We show that individual information can be represented in model space through the proper adjustment of two parameters (global coupling strength and conduction velocity), and that the underlying structural information has a strong impact on the functional outcome of the model. These findings provide a proof of concept and open the door for further advances, including the model-driven design of non-invasive brain-stimulation protocols.

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Identifying optimal working points of individual Virtual Brains: A large-scale brain network modelling study

10.1101/2020.03.26.009795 ◽

2020 ◽

Author(s):

Paul Triebkorn ◽

Joelle Zimmermann ◽

Leon Stefanovski ◽

Dipanjan Roy ◽

Ana Solodkin ◽

...

Keyword(s):

Functional Connectivity ◽

Brain Function ◽

Large Scale ◽

Brain Activity ◽

Brain Network ◽

Model Parameters ◽

Network Modelling ◽

Neural Populations ◽

Global Coupling ◽

The Brain

AbstractUsing The Virtual Brain (TVB, thevirtualbrian.org) simulation platform, we explored for 50 individual adult human brains (ages 18-80), how personalized connectome based brain network modelling captures various empirical observations as measured by functional magnetic resonance imaging (fMRI) and electroencephalography (EEG). We compare simulated activity based on individual structural connectomes (SC) inferred from diffusion weighted imaging with fMRI and EEG in the resting state. We systematically explore the role of the following model parameters: conduction velocity, global coupling and graph theoretical features of individual SC. First, a subspace of the parameter space is identified for each subject that results in realistic brain activity, i.e. reproducing the following prominent features of empirical EEG-fMRI activity: topology of resting-state fMRI functional connectivity (FC), functional connectivity dynamics (FCD), electrophysiological oscillations in the delta (3-4 Hz) and alpha (8-12 Hz) frequency range and their bimodality, i.e. low and high energy modes. Interestingly, FCD fit, bimodality and static FC fit are highly correlated. They all show their optimum in the same range of global coupling. In other words, only when our local model is in a bistable regime we are able to generate switching of modes in our global network. Second, our simulations reveal the explicit network mechanisms that lead to electrophysiological oscillations, their bimodal behaviour and inter-regional differences. Third, we discuss biological interpretability of the Stefanescu-Jirsa-Hindmarsh-Rose-3D model when embedded inside the large-scale brain network and mechanisms underlying the emergence of bimodality of the neural signal.With the present study, we set the cornerstone for a systematic catalogue of spatiotemporal brain activity regimes generated with the connectome-based brain simulation platform The Virtual Brain.Author SummaryIn order to understand brain dynamics we use numerical simulations of brain network models. Combining the structural backbone of the brain, that is the white matter fibres connecting distinct regions in the grey matter, with dynamical systems describing the activity of neural populations we are able to simulate brain function on a large scale. In order to make accurate prediction with this network, it is crucial to determine optimal model parameters. We here use an explorative approach to adjust model parameters to individual brain activity, showing that subjects have their own optimal point in the parameter space, depending on their brain structure and function. At the same time, we investigate the relation between bistable phenomena on the scale of neural populations and the changed in functional connectivity on the brain network scale. Our results are important for future modelling approaches trying to make accurate predictions of brain function.

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Electrical Brain Activity and Its Functional Connectivity in the Physical Execution of Modern Jazz Dance

Frontiers in Psychology ◽

10.3389/fpsyg.2020.586076 ◽

2020 ◽

Vol 11 ◽

Author(s):

Johanna Wind ◽

Fabian Horst ◽

Nikolas Rizzi ◽

Alexander John ◽

Wolfgang I. Schöllhorn

Keyword(s):

Functional Connectivity ◽

Brain Activity ◽

Well Being ◽

Whole Body ◽

Acute Effects ◽

Frequency Bands ◽

Alpha Frequency ◽

Electrical Brain Activity ◽

Test Conditions ◽

Modern Jazz

Besides the pure pleasure of watching a dance performance, dance as a whole-body movement is becoming increasingly popular for health-related interventions. However, the science-based evidence for improvements in health or well-being through dance is still ambiguous and little is known about the underlying neurophysiological mechanisms. This may be partly related to the fact that previous studies mostly examined the neurophysiological effects of imagination and observation of dance rather than the physical execution itself. The objective of this pilot study was to investigate acute effects of a physically executed dance with its different components (recalling the choreography and physical activity to music) on the electrical brain activity and its functional connectivity using electroencephalographic (EEG) analysis. Eleven dance-inexperienced female participants first learned a Modern Jazz Dance (MJD) choreography over three weeks (1 h sessions per week). Afterwards, the acute effects on the EEG brain activity were compared between four different test conditions: physically executing the MJD choreography with music, physically executing the choreography without music, imaging the choreography with music, and imaging the choreography without music. Every participant passed each test condition in a randomized order within a single day. EEG rest-measurements were conducted before and after each test condition. Considering time effects the physically executed dance without music revealed in brain activity analysis most increases in alpha frequency and in functional connectivity analysis in all frequency bands. In comparison, physically executed dance with music as well as imagined dance with music led to fewer increases and imagined dance without music provoked noteworthy brain activity and connectivity decreases at all frequency bands. Differences between the test conditions were found in alpha and beta frequency between the physically executed dance and the imagined dance without music as well as between the physically executed dance with and without music in the alpha frequency. The study highlights different effects of a physically executed dance compared to an imagined dance on many brain areas for all measured frequency bands. These findings provide first insights into the still widely unexplored field of neurological effects of dance and encourages further research in this direction.

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Sex-Specific Brain Responses to Imaginary Dance but Not Physical Dance: An Electroencephalography Study of Functional Connectivity and Electrical Brain Activity

Frontiers in Behavioral Neuroscience ◽

10.3389/fnbeh.2021.731881 ◽

2021 ◽

Vol 15 ◽

Author(s):

Johanna Wind ◽

Fabian Horst ◽

Nikolas Rizzi ◽

Alexander John ◽

Tamara Kurti ◽

...

Keyword(s):

Functional Connectivity ◽

Brain Activity ◽

Future Research ◽

Electrical Brain Activity ◽

Test Conditions ◽

Brain Responses ◽

Modern Jazz ◽

Before And After ◽

Specific Contact ◽

Independent Effects

To date, most neurophysiological dance research has been conducted exclusively with female participants in observational studies (i.e., participants observe or imagine a dance choreography). In this regard, the sex-specific acute neurophysiological effect of physically executed dance can be considered a widely unexplored field of research. This study examines the acute impact of a modern jazz dance choreography on brain activity and functional connectivity using electroencephalography (EEG). In a within-subject design, 11 female and 11 male participants were examined under four test conditions: physically dancing the choreography with and without music and imagining the choreography with and without music. Prior to the EEG measurements, the participants acquired the choreography over 3 weeks with one session per week. Subsequently, the participants conducted all four test conditions in a randomized order on a single day, with the EEG measurements taken before and after each condition. Differences between the male and female participants were established in brain activity and functional connectivity analyses under the condition of imagined dance without music. No statistical differences between sexes were found in the other three conditions (physically executed dance with and without music as well as imagined dance with music). Physically dancing and music seem to have sex-independent effects on the human brain. However, thinking of dance without music seems to be rather sex-specific. The results point to a promising approach to decipher sex-specific differences in the use of dance or music. This approach could further be used to achieve a more group-specific or even more individualized and situationally adapted use of dance interventions, e.g., in the context of sports, physical education, or therapy. The extent to which the identified differences are due to culturally specific attitudes in the sex-specific contact with dance and music needs to be clarified in future research.

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Towards a Brain-Sensitive Intelligent Tutoring System: Detecting Emotions from Brainwaves

Advances in Artificial Intelligence ◽

10.1155/2011/384169 ◽

2011 ◽

Vol 2011 ◽

pp. 1-13 ◽

Cited By ~ 5

Author(s):

Alicia Heraz ◽

Claude Frasson

Keyword(s):

Brain Activity ◽

Intelligent Tutoring ◽

Intelligent Tutoring System ◽

Emotional Valence ◽

Tutoring System ◽

Electrical Brain Activity ◽

Self Reports ◽

Natural Data ◽

Emotional Dimensions ◽

The Individual

This paper proposes and evaluates a multiagents system called NORA that predicts emotional attributes from learners' brainwaves within an intelligent tutoring system. The measurements from the electrical brain activity of the learner are combined with information about the learner's emotional attributes. Electroencephalogram was used to measure brainwaves and self-reports to measure the three emotional dimensions: pleasure, arousal, and dominance, the eight emotions occurring during learning: anger, boredom, confusion, contempt curious, disgust, eureka, and frustration, and the emotional valence positive for learning and negative for learning. The system is evaluated on natural data, and it achieves an accuracy of over 63%, significantly outperforming classification using the individual modalities and several other combination schemes.

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Noise-driven multistability versus deterministic chaos in phenomenological semi-empirical models of whole-brain activity

10.1101/2020.07.31.231712 ◽

2020 ◽

Author(s):

Juan Piccinini ◽

Ignacio Perez Ipina ◽

Helmut Laufs ◽

Morten Kringelbach ◽

Gustavo Deco ◽

...

Keyword(s):

Functional Connectivity ◽

Conceptual Understanding ◽

Empirical Data ◽

Large Scale ◽

Brain Activity ◽

Deterministic Chaos ◽

Building Blocks ◽

Model Parameters ◽

Whole Brain ◽

Local Dynamics

An outstanding open problem in neuroscience is to understand how neural systems are capable of producing and sustaining complex spatiotemporal dynamics. Computational models that combine local dynamics with in vivo measurements of anatomical and functional connectivity can be used to test potential mechanisms underlying this complexity. We compared two conceptually different mechanisms: noise-driven switching between equilibrium solutions (modeled by coupled Stuart-Landau oscillators) and deterministic chaos (modeled by coupled Rossler oscillators). We found that both models struggled to simultaneously reproduce multiple observables computed from the empirical data. This issue was especially manifest in the case of noise-driven dynamics close to a bifurcation, which imposed overly strong constraints on the optimal model parameters. In contrast, the chaotic model could produce complex behavior over an ampler range of parameters, thus being capable of capturing multiple observables at the same time with good performance. Our observations support the view of the brain as a non-equilibrium system able to produce endogenous variability. We presented a simple model capable of jointly reproducing functional connectivity computed at different temporal scales. Besides adding to our conceptual understanding of brain complexity, our results inform and constraint the future development of biophysically realistic large-scale models.The quote “What I cannot create, I do not understand” was found written in the blackboard of celebrated physicist Richard Feynman at the time of his death. This sentence suggests a way forward for neuroscientists interested in unravelling the principles behind the richness and complexity of spontaneous brain dynamics. Over the last decades, tremendous advances in neuroimaging enabled the construction of whole-brain activity models with real predictive power in the statistical sense. It is now possible to create realistic complex dynamics, instead of passively screening for their presence in neuroimaging data. We contrasted two different types of building blocks (i.e. two choices of local dynamics) and tested their capacity to reproduce the empirical data, with the purpose of increasing our conceptual understanding of the mechanisms behind large-scale spontaneous activity in the human brain.

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Skipping Breakfast Affects the Early Steps of Cognitive Processing

Journal of Psychophysiology ◽

10.1027/0269-8803/a000214 ◽

2019 ◽

Vol 33 (2) ◽

pp. 109-118

Author(s):

Andrés Antonio González-Garrido ◽

Jacobo José Brofman-Epelbaum ◽

Fabiola Reveca Gómez-Velázquez ◽

Sebastián Agustín Balart-Sánchez ◽

Julieta Ramos-Loyo

Keyword(s):

Working Memory ◽

Cognitive Processing ◽

Task Difficulty ◽

Brain Activity ◽

Reaction Times ◽

Brain Potentials ◽

Brain Functioning ◽

Attentional Processes ◽

Electrical Brain Activity ◽

Skipping Breakfast

Abstract. It has been generally accepted that skipping breakfast adversely affects cognition, mainly disturbing the attentional processes. However, the effects of short-term fasting upon brain functioning are still unclear. We aimed to evaluate the effect of skipping breakfast on cognitive processing by studying the electrical brain activity of young healthy individuals while performing several working memory tasks. Accordingly, the behavioral results and event-related brain potentials (ERPs) of 20 healthy university students (10 males) were obtained and compared through analysis of variances (ANOVAs), during the performance of three n-back working memory (WM) tasks in two morning sessions on both normal (after breakfast) and 12-hour fasting conditions. Significantly fewer correct responses were achieved during fasting, mainly affecting the higher WM load task. In addition, there were prolonged reaction times with increased task difficulty, regardless of breakfast intake. ERP showed a significant voltage decrement for N200 and P300 during fasting, while the amplitude of P200 notably increased. The results suggest skipping breakfast disturbs earlier cognitive processing steps, particularly attention allocation, early decoding in working memory, and stimulus evaluation, and this effect increases with task difficulty.

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An Adaptive and Learning System for Feedback-Controlled Evoked Response Studies

Methods of Information in Medicine ◽

10.1055/s-0038-1635302 ◽

1981 ◽

Vol 20 (03) ◽

pp. 169-173

Author(s):

J. Wagner ◽

G. Pfurtscheixer

Keyword(s):

Brain Activity ◽

Evoked Response ◽

Learning System ◽

Time Interval ◽

Electrical Brain Activity ◽

Long Time ◽

The Subject ◽

Eeg Power ◽

Stimulation System ◽

Background Eeg

The shape, latency and amplitude of changes in electrical brain activity related to a stimulus (Evoked Potential) depend both on the stimulus parameters and on the background EEG at the time of stimulation. An adaptive, learnable stimulation system is introduced, whereby the subject is stimulated (e.g. with light), whenever the EEG power is subthreshold and minimal. Additionally, the system is conceived in such a way that a certain number of stimuli could be given within a particular time interval. Related to this time criterion, the threshold specific for each subject is calculated at the beginning of the experiment (preprocessing) and adapted to the EEG power during the processing mode because of long-time fluctuations and trends in the EEG. The process of adaptation is directed by a table which contains the necessary correction numbers for the threshold. Experiences of the stimulation system are reflected in an automatic correction of this table. Because the corrected and improved table is stored after each experiment and is used as the starting table for the next experiment, the system >learns<. The system introduced here can be used both for evoked response studies and for alpha-feedback experiments.

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Automated System for Epileptic Seizures Prediction based on Multi-Channel Recordings of Electrical Brain Activity

Information and Control Systems ◽

10.31799/1684-8853-2018-4-115-122 ◽

2018 ◽

pp. 115-122 ◽

Cited By ~ 1

Author(s):

V. A. Maksimenko ◽

A. A. Harchenko ◽

A. Lüttjohann

Keyword(s):

Epileptic Seizure ◽

Brain Activity ◽

Epileptic Seizures ◽

Automated System ◽

Brain Computer Interfaces ◽

Electrical Brain Activity ◽

Computer Interfaces ◽

Prediction And Prevention ◽

The Brain

Introduction: Now the great interest in studying the brain activity based on detection of oscillatory patterns on the recorded data of electrical neuronal activity (electroencephalograms) is associated with the possibility of developing brain-computer interfaces. Braincomputer interfaces are based on the real-time detection of characteristic patterns on electroencephalograms and their transformation into commands for controlling external devices. One of the important areas of the brain-computer interfaces application is the control of the pathological activity of the brain. This is in demand for epilepsy patients, who do not respond to drug treatment.Purpose: A technique for detecting the characteristic patterns of neural activity preceding the occurrence of epileptic seizures.Results:Using multi-channel electroencephalograms, we consider the dynamics of thalamo-cortical brain network, preceded the occurrence of an epileptic seizure. We have developed technique which allows to predict the occurrence of an epileptic seizure. The technique has been implemented in a brain-computer interface, which has been tested in-vivo on the animal model of absence epilepsy.Practical relevance:The results of our study demonstrate the possibility of epileptic seizures prediction based on multichannel electroencephalograms. The obtained results can be used in the development of neurointerfaces for the prediction and prevention of seizures of various types of epilepsy in humans.

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Delta band activity contributes to the identification of command following in disorder of consciousness

Scientific Reports ◽

10.1038/s41598-021-95818-6 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Gonzalo Rivera-Lillo ◽

Emmanuel A. Stamatakis ◽

Tristan A. Bekinschtein ◽

David K. Menon ◽

Srivas Chennu

Keyword(s):

Functional Neuroimaging ◽

Brain Activity ◽

Disorder Of Consciousness ◽

Fine Grained ◽

Electrical Brain Activity ◽

Delta Band ◽

Delta Modulation ◽

Command Following ◽

Band Activity

AbstractThe overt or covert ability to follow commands in patients with disorders of consciousness is considered a sign of awareness and has recently been defined as cortically mediated behaviour. Despite its clinical relevance, the brain signatures of the perceptual processing supporting command following have been elusive. This multimodal study investigates the temporal spectral pattern of electrical brain activity to identify features that differentiated healthy controls from patients both able and unable to follow commands. We combined evidence from behavioural assessment, functional neuroimaging during mental imagery and high-density electroencephalography collected during auditory prediction, from 21 patients and 10 controls. We used a penalised regression model to identify command following using features from electroencephalography. We identified seven well-defined spatiotemporal signatures in the delta, theta and alpha bands that together contribute to identify DoC subjects with and without the ability to follow command, and further distinguished these groups of patients from controls. A fine-grained analysis of these seven signatures enabled us to determine that increased delta modulation at the frontal sensors was the main feature in command following patients. In contrast, higher frequency theta and alpha modulations differentiated controls from both groups of patients. Our findings highlight a key role of spatiotemporally specific delta modulation in supporting cortically mediated behaviour including the ability to follow command. However, patients able to follow commands nevertheless have marked differences in brain activity in comparison with healthy volunteers.

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A detailed analysis of anatomical plausibility of crossed and uncrossed streamline rendition of the dentato-rubro-thalamic tract (DRT(T)) in a commercial stereotactic planning system

Acta Neurochirurgica ◽

10.1007/s00701-021-04890-4 ◽

2021 ◽

Author(s):

Volker A. Coenen ◽

Bastian E. Sajonz ◽

Peter C. Reinacher ◽

Christoph P. Kaller ◽

Horst Urbach ◽

...

Keyword(s):

Surgical Planning ◽

Ground Truth ◽

Planning System ◽

Single Subject ◽

Fiber Structure ◽

Native Space ◽

Volume Of Interest ◽

Mri Scans ◽

Magnetic Resonance Imaging Mri ◽

The Individual

Abstract Background An increasing number of neurosurgeons use display of the dentato-rubro-thalamic tract (DRT) based on diffusion weighted imaging (dMRI) as basis for their routine planning of stimulation or lesioning approaches in stereotactic tremor surgery. An evaluation of the anatomical validity of the display of the DRT with respect to modern stereotactic planning systems and across different tracking environments has not been performed. Methods Distinct dMRI and anatomical magnetic resonance imaging (MRI) data of high and low quality from 9 subjects were used. Six subjects had repeated MRI scans and therefore entered the analysis twice. Standardized DICOM structure templates for volume of interest definition were applied in native space for all investigations. For tracking BrainLab Elements (BrainLab, Munich, Germany), two tensor deterministic tracking (FT2), MRtrix IFOD2 (https://www.mrtrix.org), and a global tracking (GT) approach were used to compare the display of the uncrossed (DRTu) and crossed (DRTx) fiber structure after transformation into MNI space. The resulting streamlines were investigated for congruence, reproducibility, anatomical validity, and penetration of anatomical way point structures. Results In general, the DRTu can be depicted with good quality (as judged by waypoints). FT2 (surgical) and GT (neuroscientific) show high congruence. While GT shows partly reproducible results for DRTx, the crossed pathway cannot be reliably reconstructed with the other (iFOD2 and FT2) algorithms. Conclusion Since a direct anatomical comparison is difficult in the individual subjects, we chose a comparison with two research tracking environments as the best possible “ground truth.” FT2 is useful especially because of its manual editing possibilities of cutting erroneous fibers on the single subject level. An uncertainty of 2 mm as mean displacement of DRTu is expectable and should be respected when using this approach for surgical planning. Tractographic renditions of the DRTx on the single subject level seem to be still illusive.

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