Mapping dynamical properties of cortical microcircuits using robotized TMS and EEG: Towards functional cytoarchitectonics

International audienceBrain dynamics at rest depend on the large-scale interactions between oscillating cortical microcircuits arranged into macrocolumns. Cytoarchitectonic studies have shown that the structure of those microcircuits differs between cortical regions, but very little is known about interregional differences of their intrinsic dynamics at a macro-scale in human. We developed here a new method aiming at mapping the dynamical properties of cortical microcircuits non-invasively using the coupling between robotized transcranial magnetic stimulation and elec-troencephalography. We recorded the responses evoked by the stimulation of 18 cortical targets largely covering the accessible neocortex in 22 healthy volunteers. Specific data processing methods were developed to map the local source activity of each cortical target, which showed interregional differences with very good interhemi-spheric reproducibility. Functional signatures of cortical microcircuits were further studied using spatio-temporal decomposition of local source activities in order to highlight principal brain modes. The identified brain modes revealed that cortical areas with similar intrinsic dynamical properties could be distributed either locally or not, with a spatial signature that was somewhat reminiscent of resting state networks. Our results provide the proof of concept of " functional cytoarchitectonics " , that would guide the parcellation of the human cortex using not only its cytoarchitecture but also its intrinsic responses to local perturbations. This opens new avenues for brain modelling and physiopathology readouts

Relating Alpha Power and Phase to Population Firing and Hemodynamic Activity Using a Thalamo-cortical Neural Mass Model

Oscillations are ubiquitous phenomena in the animal and human brain. Among them, the alpha rhythm in human EEG is one of the most prominent examples. However, its precise mechanisms of generation are still poorly understood. It was mainly this lack of knowledge that motivated a number of simultaneous electroencephalography (EEG) – functional magnetic resonance imaging (fMRI) studies. This approach revealed how oscillatory neuronal signatures such as the alpha rhythm are paralleled by changes of the blood oxygenation level dependent (BOLD) signal. Several such studies revealed a negative correlation between the alpha rhythm and the hemodynamic BOLD signal in visual cortex and a positive correlation in the thalamus. In this study we explore the potential generative mechanisms that lead to those observations. We use a bursting capable Stefanescu-Jirsa 3D (SJ3D) neural-mass model that reproduces a wide repertoire of prominent features of local neuronal-population dynamics. We construct a thalamo-cortical network of coupled SJ3D nodes considering excitatory and inhibitory directed connections. The model suggests that an inverse correlation between cortical multi-unit activity, i.e. the firing of neuronal populations, and narrow band local field potential oscillations in the alpha band underlies the empirically observed negative correlation between alpha-rhythm power and fMRI signal in visual cortex. Furthermore the model suggests that the interplay between tonic and bursting mode in thalamus and cortex is critical for this relation. This demonstrates how biophysically meaningful modelling can generate precise and testable hypotheses about the underpinnings of large-scale neuroimaging signals

Relating Alpha Power and Phase to Population Firing and Hemodynamic Activity Using a Thalamo-cortical Neural Mass Model

International audienceOscillations are ubiquitous phenomena in the animal and human brain. Among them, the alpha rhythm in human EEG is one of the most prominent examples. However, its precise mechanisms of generation are still poorly understood. It was mainly this lack of knowledge that motivated a number of simultaneous electroencephalography (EEG) – functional magnetic resonance imaging (fMRI) studies. This approach revealed how oscillatory neuronal signatures such as the alpha rhythm are paralleled by changes of the blood oxygenation level dependent (BOLD) signal. Several such studies revealed a negative correlation between the alpha rhythm and the hemodynamic BOLD signal in visual cortex and a positive correlation in the thalamus. In this study we explore the potential generative mechanisms that lead to those observations. We use a bursting capable Stefanescu-Jirsa 3D (SJ3D) neural-mass model that reproduces a wide repertoire of prominent features of local neuronal-population dynamics. We construct a thalamo-cortical network of coupled SJ3D nodes considering excitatory and inhibitory directed connections. The model suggests that an inverse correlation between cortical multi-unit activity, i.e. the firing of neuronal populations , and narrow band local field potential oscillations in the alpha band underlies the empirically observed negative correlation between alpha-rhythm power and fMRI signal in visual cortex. Furthermore the model suggests that the interplay between tonic and bursting mode in thalamus and cortex is critical for this relation. This demonstrates how biophysically meaningful modelling can generate precise and testable hypotheses about the underpinnings of large-scale neuroimaging signals

Brain network, modelling and corresponding EEG patterns for health and disease states

EEG is a significant tool used to capture normal and abnormal cerebral electrical activities in human brain. To understand and test complex hypotheses about the mechanisms of their generation, various model and modelling approaches have been proposed and developed. Among these models and approaches, a new type of network model has emerged known as large-scale brain network model (LSBNM). LSBNM is becoming increasingly important in understanding, studying and testing the mechanisms of the generation of normal and abnormal oscillatory activities of the human brain. It also offers unique predictive tools for studying disease states and brain abnormalities. However, there are still many limitations in the existing LSBNM approaches. Hence, developing novel methods for LSBNM leads to the exploration, generation and prediction of a new and rich repertoire of healthy and disease rhythmic activities in the human brain. The aim of this project is to develop LSBNM to include new versions of network models comprising various human cerebral areas in the left and right hemispheres. First, two network models at multi scale are developed to generate EEG patterns for health states: alpha rhythms with a low frequency at 7Hz and, and the alpha band of EEG rhythms at different ranges of frequencies 7–8 Hz, 8 9 Hz and 10–11 Hz. Second, a new network model for simulating multi-bands of EEG patterns: delta–range frequency of (1-4 Hz), theta at a frequency of (4-7Hz) and diverse narrowband oscillations ranging from delta to theta (0-5Hz) is introduced. Third, novel brain network models are simulated and used to predict the abnormal electrical activity such as oscillations observed in the epileptic brain. The design and simulation of each of the network models are implemented using the unique neuro informatics platform: The Virtual Brain (TVB). This project made significant contributions to brain modelling, in particularly to the understanding of neural activity in the human brain at multi levels of scale. Further, it emphasises the role of structural connectivity of the connectome on emerging normal and abnormal dynamics of brain oscillations, as well as affirming that modelling with TVB can provide reliable neuroimaging data such as EEGS for the healthy and diseased brain. In particular, the results of this study help researchers and physicians studying large-scale brain activity associated with lower and higher alpha oscillations and the delta waves of Stages 3 and 4 of the sleep and theta waves of Stages 1 and 2 of sleep. Moreover, they will be able to assist researchers and clinical doctors in the field of epilepsy to understand the complex neural mechanisms generating abnormal oscillatory activities and, thus, may open up new avenues towards the discovery of new clinical interventions related to these types of activities

An automated pipeline for constructing personalized virtual brains from multimodal neuroimaging data

AbstractLarge amounts of multimodal neuroimaging data are acquired every year worldwide. In order to extract high-dimensional information for computational neuroscience applications standardized data fusion and efficient reduction into integrative data structures are required. Such self-consistent multimodal data sets can be used for computational brain modeling to constrain models with individual measurable features of the brain, such as done with The Virtual Brain (TVB). TVB is a simulation platform that uses empirical structural and functional data to build full brain models of individual humans. For convenient model construction, we developed a processing pipeline for structural, functional and diffusion-weighted magnetic resonance imaging (MRI) and optionally electroencephalography (EEG) data. The pipeline combines several state-of-the-art neuroinformatics tools to generate subject-specific cortical and subcortical parcellations, surface-tessellations, structural and functional connectomes, lead field matrices, electrical source activity estimates and region-wise aggregated blood oxygen level dependent (BOLD) functional MRI (fMRI) time-series. The output files of the pipeline can be directly uploaded to TVB to create and simulate individualized large-scale network models that incorporate intra- and intercortical interaction on the basis of cortical surface triangulations and white matter tractograpy. We detail the pitfalls of the individual processing streams and discuss ways of validation. With the pipeline we also introduce novel ways of estimating the transmission strengths of fiber tracts in whole-brain structural connectivity (SC) networks and compare the outcomes of different tractography or parcellation approaches. We tested the functionality of the pipeline on 50 multimodal data sets. In order to quantify the robustness of the connectome extraction part of the pipeline we computed several metrics that quantify its rescan reliability and compared them to other tractography approaches. Together with the pipeline we present several principles to guide future efforts to standardize brain model construction. The code of the pipeline and the fully processed data sets are made available to the public via The Virtual Brain website (thevirtualbrain.org) and via github (https://github.com/BrainModes/TVB-empirical-data-pipeline). Furthermore, the pipeline can be directly used with High Performance Computing (HPC) resources on the Neuroscience Gateway Portal (http://www.nsgportal.org) through a convenient web-interface

Cognitive Decay And Memory Recall During Long Duration Spaceflight

This dissertation aims to advance the efficacy of Long-Duration Space Flight (LDSF) pre-flight and in-flight training programs, acknowledging existing knowledge gaps in NASA\u27s methodologies. The research\u27s objective is to optimize the cognitive workload of LDSF crew members, enhance their neurocognitive functionality, and provide more meaningful work experiences, particularly for Mars missions.The study addresses identified shortcomings in current training and learning strategies and simulation-based training systems, focusing on areas requiring quantitative measures for astronaut proficiency and training effectiveness assessment. The project centers on understanding cognitive decay and memory loss under LDSF-related stressors, seeking to establish when such cognitive decline exceeds acceptable performance levels throughout mission phases. The research acknowledges the limitations of creating a near-orbit environment due to resource constraints and the need to develop engaging tasks for test subjects. Nevertheless, it underscores the potential impact on future space mission training and other high-risk professions. The study further explores astronaut training complexities, the challenges encountered in LDSF missions, and the cognitive processes involved in such demanding environments. The research employs various cognitive and memory testing events, integrating neuroimaging techniques to understand cognition\u27s neural mechanisms and memory. It also explores Rasmussen\u27s S-R-K behaviors and Brain Network Theory’s (BNT) potential for measuring forgetting, cognition, and predicting training needs. The multidisciplinary approach of the study reinforces the importance of integrating insights from cognitive psychology, behavior analysis, and brain connectivity research. Research experiments were conducted at the University of North Dakota\u27s Integrated Lunar Mars Analog Habitat (ILMAH), gathering data from selected subjects via cognitive neuroscience tools and Electroencephalography (EEG) recordings to evaluate neurocognitive performance. The data analysis aimed to assess brain network activations during mentally demanding activities and compare EEG power spectra across various frequencies, latencies, and scalp locations. Despite facing certain challenges, including inadequacies of the current adapter boards leading to analysis failure, the study provides crucial lessons for future research endeavors. It highlights the need for swift adaptation, continual process refinement, and innovative solutions, like the redesign of adapter boards for high radio frequency noise environments, for the collection of high-quality EEG data. In conclusion, while the research did not reveal statistically significant differences between the experimental and control groups, it furnished valuable insights and underscored the need to optimize astronaut performance, well-being, and mission success. The study contributes to the ongoing evolution of training methodologies, with implications for future space exploration endeavors

1993 Annual report on scientific programs: A broad research program on the sciences of complexity

This report provides a summary of many of the research projects completed by the Santa Fe Institute (SFI) during 1993. These research efforts continue to focus on two general areas: the study of, and search for, underlying scientific principles governing complex adaptive systems, and the exploration of new theories of computation that incorporate natural mechanisms of adaptation (mutation, genetics, evolution)

The interdependence of nature and nurture in the establishment and maintenance of mind: an eco-dynamic paradigm

This dissertation makes the case that the human mind is established and maintained by the interdependence or enmeshment of multiple complex, dynamic systems; biological, social, and technological. These are not merely peripheral but rather, jointly are constitutive of mind. I develop this thesis in what I call the “eco-dynamic paradigm,” which modifies and supplements enactivism. This dissertation has two main theses: first, mind is established and maintained by features that draw on the resources of the brain, body and the contextual environment in which one is embedded. The second thesis is that Dynamic Systems Theory is an important resource in modelling, explaining and analysing the complex, dynamic relationships within and between scales of brain, body and contextual environment. I use the language and concepts of Dynamic Systems Theory qualitatively to describe the dynamics of brain, body, environmental relationships. Methodologically, this dissertation is both interdisciplinary and cross-cultural. I refer to Indo-Tibetan Buddhism as an excellent example of a culture whose goal is to transform the mind to clarity by utilising a symbiotic package of meditation and visualisation practices, teachings, rituals and philosophies. These elements together provide an interconnected web which are used to support and assist the cognitive transformation of the practitioner. The conceptual and practical elements of Indo-Tibetan Buddhism, the relations between them and even the process of cognitive transformation can also be analysed by Dynamic Systems Theory. Death and dying provide a fulcrum in which the resources of the eco-dynamic paradigm are best utilised. Indo-Tibetan Buddhist practices, concepts and philosophy related to the nature of the mind come into contrast with those of Western medical science sharply in death and dying. The challenge posed to medical science is to study and explain what might appear to be anomalous cases of alleged cognition or mental activity without brain function in near death experience. A specific programme of research is suggested in which the nature of the mind is explored neurophenomenologically.