scholarly journals Fundamental Dynamical Modes Underlying Human Brain Synchronization

2012 ◽  
Vol 2012 ◽  
pp. 1-8 ◽  
Author(s):  
Catalina Alvarado-Rojas ◽  
Michel Le Van Quyen
Keyword(s):  
Large Scale ◽  
Brain Regions ◽  
Electrode Placement ◽  
High Dimensional ◽  
Sleep Cycle ◽  
Epileptic Patients ◽  
Brain States ◽  
Intracranial Recordings ◽  
Characteristic Dynamic

Little is known about the long-term dynamics of widely interacting cortical and subcortical networks during the wake-sleep cycle. Using large-scale intracranial recordings of epileptic patients during seizure-free periods, we investigated local- and long-range synchronization between multiple brain regions over several days. For such high-dimensional data, summary information is required for understanding and modelling the underlying dynamics. Here, we suggest that a compact yet useful representation is given by a state space based on the first principal components. Using this representation, we report, with a remarkable similarity across the patients with different locations of electrode placement, that the seemingly complex patterns of brain synchrony during the wake-sleep cycle can be represented by a small number of characteristic dynamic modes. In this space, transitions between behavioral states occur through specific trajectories from one mode to another. These findings suggest that, at a coarse level of temporal resolution, the different brain states are correlated with several dominant synchrony patterns which are successively activated across wake-sleep states.

scholarly journals Large-Scale Chronically Implantable Precision Motorized Microdrive Array for Freely Behaving Animals

2008 ◽  
Vol 100 (4) ◽  
pp. 2430-2440 ◽  
Author(s):  
Jun Yamamoto ◽  
Matthew A. Wilson
Keyword(s):  
Single Unit ◽  
Large Scale ◽  
Neural Circuits ◽  
Brain Regions ◽  
Unit Recording ◽  
Chronic Recording ◽  
Large Numbers ◽  
Freely Behaving

Multiple single-unit recording has become one of the most powerful in vivo electro-physiological techniques for studying neural circuits. The demand has been increasing for small and lightweight chronic recording devices that allow fine adjustments to be made over large numbers of electrodes across multiple brain regions. To achieve this, we developed precision motorized microdrive arrays that use a novel motor multiplexing headstage to dramatically reduce wiring while preserving precision of the microdrive control. Versions of the microdrive array were chronically implanted on both rats (21 microdrives) and mice (7 microdrives), and relatively long-term recordings were taken.

scholarly journals Highly scalable multichannel mesh electronics for stable chronic brain electrophysiology

2017 ◽  
Vol 114 (47) ◽  
pp. E10046-E10055 ◽  
Author(s):  
Tian-Ming Fu ◽  
Guosong Hong ◽  
Robert D. Viveros ◽  
Tao Zhou ◽  
Charles M. Lieber
Keyword(s):  
Large Scale ◽  
Single Neuron ◽  
Neurological Diseases ◽  
Field Potential ◽  
Brain Regions ◽  
Long Term Stability ◽  
High Density Recording ◽  
Local Circuits ◽  
Key Aspects

Implantable electrical probes have led to advances in neuroscience, brain−machine interfaces, and treatment of neurological diseases, yet they remain limited in several key aspects. Ideally, an electrical probe should be capable of recording from large numbers of neurons across multiple local circuits and, importantly, allow stable tracking of the evolution of these neurons over the entire course of study. Silicon probes based on microfabrication can yield large-scale, high-density recording but face challenges of chronic gliosis and instability due to mechanical and structural mismatch with the brain. Ultraflexible mesh electronics, on the other hand, have demonstrated negligible chronic immune response and stable long-term brain monitoring at single-neuron level, although, to date, it has been limited to 16 channels. Here, we present a scalable scheme for highly multiplexed mesh electronics probes to bridge the gap between scalability and flexibility, where 32 to 128 channels per probe were implemented while the crucial brain-like structure and mechanics were maintained. Combining this mesh design with multisite injection, we demonstrate stable 128-channel local field potential and single-unit recordings from multiple brain regions in awake restrained mice over 4 mo. In addition, the newly integrated mesh is used to validate stable chronic recordings in freely behaving mice. This scalable scheme for mesh electronics together with demonstrated long-term stability represent important progress toward the realization of ideal implantable electrical probes allowing for mapping and tracking single-neuron level circuit changes associated with learning, aging, and neurodegenerative diseases.

scholarly journals Computational modelling of the long-term effects of brain stimulation on the local and global structural connectivity of epileptic patients

2019 ◽  
Author(s):  
Emmanouil Giannakakis ◽  
Frances Hutchings ◽  
Christoforos A. Papasavvas ◽  
Cheol E. Han ◽  
Bernd Weber ◽  
...  
Keyword(s):  
Focal Epilepsy ◽  
Brain Connectivity ◽  
Diffusion Imaging ◽  
Structural Connectivity ◽  
Computational Modelling ◽  
Brain Regions ◽  
Long Term Effects ◽  
Surgery Outcome ◽  
Epileptic Patients

AbstractIn patients with drug resistant focal epilepsy, targeted weak stimulation of the affected brain regions has been proposed as an alternative to surgery. However, the effectiveness of stimulation as a treatment presents great variation from patient to patient. In this study, brain activity is simulated for a period of one day using a network of Wilson-Cowan oscillators coupled according to diffusion imaging based structural connectivity. We use this computational model to examine the potential long-term effects of stimulation on brain connectivity. Our findings indicate that the overall simulated effect of stimulation is heavily dependent on the excitability of the stimulated regions. Additionally, stimulation seems to lead to long-term effects in the connectivity of secondary (non-stimulated) regions in epileptic patients. These effects are correlated with a worse surgery outcome in some patients, which suggests that long-term simulations could be used as a tool to determine suitability for surgery/stimulation.

scholarly journals Multimodal mapping of neural activity and cerebral blood flow reveals long-lasting neurovascular dissociations after small-scale strokes

2020 ◽  
Author(s):  
Fei He ◽  
Colin Sullender ◽  
Hanlin Zhu ◽  
Michael R. Williamson ◽  
Xue Li ◽  
...  
Keyword(s):  
Blood Flow ◽  
Cerebral Blood Flow ◽  
Neural Activity ◽  
Brain Regions ◽  
Small Scale ◽  
Spatially Resolved ◽  
Relative Value ◽  
Brain States ◽  
Neuroimaging Techniques

AbstractNeurovascular coupling, the close spatial and temporal relationship between neural activity and hemodynamics, is disrupted in pathological brain states. To understand the altered neurovascular relationship in brain disorders, longitudinal, simultaneous mapping of neural activity and hemodynamics is critical yet challenging to achieve. Here, we employ a multimodal neural platform in a mouse model of stroke and realize long-term, spatially-resolved tracking of intracortical neural activity and cerebral blood flow in the same brain regions. We observe a pronounced neurovascular dissociation that occurs immediately after small-scale strokes, becomes the most severe a few days after, lasts into chronic periods, and varies with the level of ischemia. Neuronal deficits extend spatiotemporally whereas restoration of cerebral blood flow occurs sooner and reaches a higher relative value. Our findings reveal the neurovascular impact of mini-strokes and inform the limitation of neuroimaging techniques that infer neural activity from hemodynamic responses.

scholarly journals Multimodal mapping of neural activity and cerebral blood flow reveals long-lasting neurovascular dissociations after small-scale strokes

2020 ◽  
Vol 6 (21) ◽  
pp. eaba1933 ◽  
Author(s):  
Fei He ◽  
Colin T. Sullender ◽  
Hanlin Zhu ◽  
Michael R. Williamson ◽  
Xue Li ◽  
...  
Keyword(s):  
Blood Flow ◽  
Cerebral Blood Flow ◽  
Neural Activity ◽  
Brain Regions ◽  
Small Scale ◽  
Spatially Resolved ◽  
Relative Value ◽  
Brain States ◽  
Neuroimaging Techniques

Neurovascular coupling, the close spatial and temporal relationship between neural activity and hemodynamics, is disrupted in pathological brain states. To understand the altered neurovascular relationship in brain disorders, longitudinal, simultaneous mapping of neural activity and hemodynamics is critical yet challenging to achieve. Here, we use a multimodal neural platform in a mouse model of stroke and realize long-term, spatially resolved tracking of intracortical neural activity and cerebral blood flow in the same brain regions. We observe a pronounced neurovascular dissociation that occurs immediately after small-scale strokes, becomes the most severe a few days after, lasts into chronic periods, and varies with the level of ischemia. Neuronal deficits extend spatiotemporally, whereas restoration of cerebral blood flow occurs sooner and reaches a higher relative value. Our findings reveal the neurovascular impact of ministrokes and inform the limitation of neuroimaging techniques that infer neural activity from hemodynamic responses.

scholarly journals Comparing Task-Relevant Information Across Different Methods of Extracting Functional Connectivity

2018 ◽  
Author(s):  
Sophie Benitez Stulz ◽  
Andrea Insabato ◽  
Gustavo Deco ◽  
Matthieu Gilson ◽  
Mario Senden
Keyword(s):  
Functional Connectivity ◽  
Large Scale ◽  
Cross Validation ◽  
Activity Patterns ◽  
Relevant Information ◽  
Brain Regions ◽  
Classification Performance ◽  
Phase Coherence ◽  
Brain State ◽  
Brain States

AbstractThe concept of brain states, functionally relevant large-scale activity patterns, has become popular in neuroimaging. Not all components of such patterns are equally characteristic for each brain state, but machine learning provides a possibility for extracting and comparing the structure of brain states from functional data. However, their characterization in terms of functional connectivity measures varies widely, from cross-correlation to phase coherence, and the idea that different measures provide similar or coherent information is a common assumption made in neuroimaging. Here, we compare the brain state signatures extracted from of phase coherence, pairwise covariance, correlation, regularized covariance and regularized precision for a dataset of subjects performing five different cognitive tasks. In addition, we compare the classification performance in identifying the tasks for each connectivity measure. The measures are evaluated in their ability to discriminate the five tasks with two types of cross-validation: within-subject cross-validation, which reflects the stability of the signature over time; and between-subject cross-validation, which aims at extracting signatures that generalize across subjects. Secondly, we compare the informative features (connections or links between brain regions/areas) across measures to test the assumption that similar information is obtained about brain state signatures from different connectivity measures. In our results, the different types of cross-validation give different classification performance and emphasize that functional connectivity measures on fMRI require observation windows of sufficient duration. Furthermore, we find that informative links for the classification, meaning changes between tasks that are consistent across subjects, are entirely uncorrelated between BOLD correlations and covariances. These results indicate that the corresponding FC signature can strongly differ across FC methods used and that interpretation is subject to caution in terms of subnetworks related to a task.

scholarly journals Arousal impacts distributed hubs modulating the integration of brain functional connectivity

2021 ◽  
Author(s):  
Kangjoo Lee ◽  
Corey Horien ◽  
David O'Connor ◽  
Bronwen Garand-Sheridan ◽  
Fuyuze Tokoglu ◽  
...  
Keyword(s):  
Large Scale ◽  
Brain Activity ◽  
Brain Regions ◽  
Brain State ◽  
Network Integration ◽  
State Dependent ◽  
Show Evidence ◽  
Brain States ◽  
High Level ◽  
The Impact

Even when subjects are at rest, it is thought that brain activity is organized into distinct brain states during which reproducible patterns are observable. Yet, it is unclear how to define or distinguish different brain states. A potential source of brain state variation is arousal, which may play a role in modulating functional interactions between brain regions. Here, we use simultaneous resting state functional magnetic resonance imaging and pupillometry to study the impact of arousal levels indexed by pupil area on the integration of large-scale brain networks. We employ a novel sparse dictionary learning-based method to identify hub regions participating in between-network integration stratified by arousal, by measuring k-hubness, the number (k) of functionally overlapping networks in each brain region. We show evidence of a brain-wide decrease in between-network integration and inter-subject variability at low relative to high arousal, with differences emerging across regions of the frontoparietal, default mode, motor, limbic, and cerebellum networks. State-dependent changes in k-hubness relate to the actual patterns of network integration within these hubs, suggesting a brain state transition from high to low arousal characterized by global synchronization and reduced network overlaps. We demonstrate that arousal is not limited to specific brain areas known to be directly associated with arousal regulation, but instead has a brain-wide impact that involves high-level between-network communications.

Evolution of Large-Scale Coronal Structures

1994 ◽  
Vol 144 ◽  
pp. 29-33
Author(s):  
P. Ambrož
Keyword(s):  
Magnetic Field ◽  
Field Line ◽  
Large Scale ◽  
Coronal Magnetic Field ◽  
Source Surface ◽  
Solar Cycles ◽  
Characteristic Relationship ◽  
The Magnetic Field ◽  
Coronal Structures

AbstractThe large-scale coronal structures observed during the sporadically visible solar eclipses were compared with the numerically extrapolated field-line structures of coronal magnetic field. A characteristic relationship between the observed structures of coronal plasma and the magnetic field line configurations was determined. The long-term evolution of large scale coronal structures inferred from photospheric magnetic observations in the course of 11- and 22-year solar cycles is described.Some known parameters, such as the source surface radius, or coronal rotation rate are discussed and actually interpreted. A relation between the large-scale photospheric magnetic field evolution and the coronal structure rearrangement is demonstrated.

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