scholarly journals Basis profile curve identification to understand electrical stimulation effects in human brain networks

2021 ◽  
Author(s):  
Kai J. Miller ◽  
Klaus-Robert Müller ◽  
Dora Hermes
Keyword(s):  
Electrical Stimulation ◽  
Brain Area ◽  
Brain Networks ◽  
Single Pulse ◽  
Electrical Current ◽  
Brain Regions ◽  
Parahippocampal Gyrus ◽  
Illustrative Case ◽  
Human Patient ◽  
Brain Surface

AbstractBrain networks can be explored by delivering brief pulses of electrical current in one area while measuring voltage responses in other areas. We propose a convergent paradigm to study brain dynamics, focusing on a single brain site to observe the average effect of stimulating each of many other brain sites. Viewed in this manner, visually-apparent motifs in the temporal response shape emerge from adjacent stimulation sites. This work constructs and illustrates a data-driven approach to determine characteristic spatiotemporal structure in these response shapes, summarized by a set of unique “basis profile curves” (BPCs). Each BPC may be mapped back to underlying anatomy in a natural way, quantifying projection strength from each stimulation site using simple metrics. Our technique is demonstrated for an array of implanted brain surface electrodes in a human patient. This framework enables straightforward interpretation of single-pulse brain stimulation data, and can be applied generically to explore the diverse milieu of interactions that comprise the connectome.Author summaryWe present a new machine learning framework to probe how brain regions interact using single-pulse electrical stimulation. Unlike previous studies, this approach does not assume a form for how one brain area will respond to stimulation in another area, but rather discovers the shape of the response in time from the data. We call the set of characteristic discovered response shapes “basis profile curves” (BPCs), and show how these can be mapped back onto the brain quantitatively. An illustrative example is included from one of our human patients to characterize inputs to the parahippocampal gyrus. A code package is downloadable from https://purl.stanford.edu/rc201dv0636 so the reader may explore the technique for own data, or study sample data provided to reproduce the illustrative case presented in the manuscript.

scholarly journals Basis profile curve identification to understand electrical stimulation effects in human brain networks

2021 ◽  
Vol 17 (9) ◽  
pp. e1008710
Author(s):  
Kai J. Miller ◽  
Klaus-Robert Müller ◽  
Dora Hermes
Keyword(s):  
Brain Networks ◽  
Single Pulse ◽  
Electrical Current ◽  
Human Patient ◽  
Surface Electrodes ◽  
Brain Surface ◽  
Brain Site ◽  
Data Driven Approach ◽  
Straightforward Interpretation ◽  
Natural Way

Brain networks can be explored by delivering brief pulses of electrical current in one area while measuring voltage responses in other areas. We propose a convergent paradigm to study brain dynamics, focusing on a single brain site to observe the average effect of stimulating each of many other brain sites. Viewed in this manner, visually-apparent motifs in the temporal response shape emerge from adjacent stimulation sites. This work constructs and illustrates a data-driven approach to determine characteristic spatiotemporal structure in these response shapes, summarized by a set of unique “basis profile curves” (BPCs). Each BPC may be mapped back to underlying anatomy in a natural way, quantifying projection strength from each stimulation site using simple metrics. Our technique is demonstrated for an array of implanted brain surface electrodes in a human patient. This framework enables straightforward interpretation of single-pulse brain stimulation data, and can be applied generically to explore the diverse milieu of interactions that comprise the connectome.

scholarly journals Single-pulse electrical stimulation methodology in freely moving rat

2021 ◽  
Vol 353 ◽  
pp. 109092
Author(s):  
Eloïse Gronlier ◽  
Estelle Vendramini ◽  
Julien Volle ◽  
Agata Wozniak-Kwasniewska ◽  
Noelia Antón Santos ◽  
...  
Keyword(s):  
Electrical Stimulation ◽  
Single Pulse ◽  
Freely Moving

scholarly journals Modulation of Human Mesenchymal Stem Cells by Electrical Stimulation Using an Enzymatic Biofuel Cell

Catalysts ◽  
2021 ◽  
Vol 11 (1) ◽  
pp. 62
Author(s):  
Won-Yong Jeon ◽  
Seyoung Mun ◽  
Wei Beng Ng ◽  
Keunsoo Kang ◽  
Kyudong Han ◽  
...  
Keyword(s):  
Gene Expression ◽  
Stem Cells ◽  
Mesenchymal Stem Cells ◽  
Electrical Stimulation ◽  
Regenerative Medicine ◽  
Cell Morphology ◽  
Electrical Current ◽  
Specific Gene ◽  
Next Generation ◽  
The Impact

Enzymatic biofuel cells (EBFCs) have excellent potential as components in bioelectronic devices, especially as active biointerfaces to regulate stem cell behavior for regenerative medicine applications. However, it remains unclear to what extent EBFC-generated electrical stimulation can regulate the functional behavior of human adipose-derived mesenchymal stem cells (hAD-MSCs) at the morphological and gene expression levels. Herein, we investigated the effect of EBFC-generated electrical stimulation on hAD-MSC cell morphology and gene expression using next-generation RNA sequencing. We tested three different electrical currents, 127 ± 9, 248 ± 15, and 598 ± 75 nA/cm2, in mesenchymal stem cells. We performed transcriptome profiling to analyze the impact of EBFC-derived electrical current on gene expression using next generation sequencing (NGS). We also observed changes in cytoskeleton arrangement and analyzed gene expression that depends on the electrical stimulation. The electrical stimulation of EBFC changes cell morphology through cytoskeleton re-arrangement. In particular, the results of whole transcriptome NGS showed that specific gene clusters were up- or down-regulated depending on the magnitude of applied electrical current of EBFC. In conclusion, this study demonstrates that EBFC-generated electrical stimulation can influence the morphological and gene expression properties of stem cells; such capabilities can be useful for regenerative medicine applications such as bioelectronic devices.

scholarly journals Gray-Matter Expansion of Social Brain Networks in Individuals High in Public Self-Consciousness

2021 ◽  
Vol 11 (3) ◽  
pp. 374
Author(s):  
Tomoyo Morita ◽  
Minoru Asada ◽  
Eiichi Naito
Keyword(s):  
Gray Matter ◽  
Structural Changes ◽  
Gray Matter Volume ◽  
Brain Networks ◽  
Japanese Version ◽  
Brain Regions ◽  
The Self ◽  
Social Brain ◽  
Default Mode ◽  
Public Self Consciousness

Self-consciousness is a personality trait associated with an individual’s concern regarding observable (public) and unobservable (private) aspects of self. Prompted by previous functional magnetic resonance imaging (MRI) studies, we examined possible gray-matter expansions in emotion-related and default mode networks in individuals with higher public or private self-consciousness. One hundred healthy young adults answered the Japanese version of the Self-Consciousness Scale (SCS) questionnaire and underwent structural MRI. A voxel-based morphometry analysis revealed that individuals scoring higher on the public SCS showed expansions of gray matter in the emotion-related regions of the cingulate and insular cortices and in the default mode network of the precuneus and medial prefrontal cortex. In addition, these gray-matter expansions were particularly related to the trait of “concern about being evaluated by others”, which was one of the subfactors constituting public self-consciousness. Conversely, no relationship was observed between gray-matter volume in any brain regions and the private SCS scores. This is the first study showing that the personal trait of concern regarding public aspects of the self may cause long-term substantial structural changes in social brain networks.

scholarly journals On the structural connectivity of large-scale models of brain networks at cellular level

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Giuseppe Giacopelli ◽  
Domenico Tegolo ◽  
Emiliano Spera ◽  
Michele Migliore
Keyword(s):  
Large Scale ◽  
Brain Networks ◽  
Structural Connectivity ◽  
Network Models ◽  
Brain Regions ◽  
Cellular Level ◽  
Scale Model ◽  
Mathematical Framework ◽  
Underlying Mechanisms ◽  
Experimental Findings

AbstractThe brain’s structural connectivity plays a fundamental role in determining how neuron networks generate, process, and transfer information within and between brain regions. The underlying mechanisms are extremely difficult to study experimentally and, in many cases, large-scale model networks are of great help. However, the implementation of these models relies on experimental findings that are often sparse and limited. Their predicting power ultimately depends on how closely a model’s connectivity represents the real system. Here we argue that the data-driven probabilistic rules, widely used to build neuronal network models, may not be appropriate to represent the dynamics of the corresponding biological system. To solve this problem, we propose to use a new mathematical framework able to use sparse and limited experimental data to quantitatively reproduce the structural connectivity of biological brain networks at cellular level.

scholarly journals Reconfiguration of human evolving large-scale epileptic brain networks prior to seizures: an evaluation with node centralities

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Rieke Fruengel ◽  
Timo Bröhl ◽  
Thorsten Rings ◽  
Klaus Lehnertz
Keyword(s):  
Large Scale ◽  
Brain Networks ◽  
Brain Regions ◽  
Brain Network ◽  
Theoretical Concept ◽  
Global Network ◽  
Time Resolved ◽  
Functional Connections ◽  
Node Centrality ◽  
Network Mechanism

AbstractPrevious research has indicated that temporal changes of centrality of specific nodes in human evolving large-scale epileptic brain networks carry information predictive of impending seizures. Centrality is a fundamental network-theoretical concept that allows one to assess the role a node plays in a network. This concept allows for various interpretations, which is reflected in a number of centrality indices. Here we aim to achieve a more general understanding of local and global network reconfigurations during the pre-seizure period as indicated by changes of different node centrality indices. To this end, we investigate—in a time-resolved manner—evolving large-scale epileptic brain networks that we derived from multi-day, multi-electrode intracranial electroencephalograpic recordings from a large but inhomogeneous group of subjects with pharmacoresistant epilepsies with different anatomical origins. We estimate multiple centrality indices to assess the various roles the nodes play while the networks transit from the seizure-free to the pre-seizure period. Our findings allow us to formulate several major scenarios for the reconfiguration of an evolving epileptic brain network prior to seizures, which indicate that there is likely not a single network mechanism underlying seizure generation. Rather, local and global aspects of the pre-seizure network reconfiguration affect virtually all network constituents, from the various brain regions to the functional connections between them.

scholarly journals Static and dynamic functional connectivity supports the configuration of brain networks associated with creative cognition

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Abhishek Uday Patil ◽  
Sejal Ghate ◽  
Deepa Madathil ◽  
Ovid J. L. Tzeng ◽  
Hsu-Wen Huang ◽  
...  
Keyword(s):  
Cognitive Processes ◽  
Large Scale ◽  
Brain Networks ◽  
Blood Oxygen Level Dependent ◽  
Detection Algorithm ◽  
Brain Regions ◽  
Creative Cognition ◽  
Network Flexibility ◽  
Community Detection Algorithm ◽  
Window Approach

AbstractCreative cognition is recognized to involve the integration of multiple spontaneous cognitive processes and is manifested as complex networks within and between the distributed brain regions. We propose that the processing of creative cognition involves the static and dynamic re-configuration of brain networks associated with complex cognitive processes. We applied the sliding-window approach followed by a community detection algorithm and novel measures of network flexibility on the blood-oxygen level dependent (BOLD) signal of 8 major functional brain networks to reveal static and dynamic alterations in the network reconfiguration during creative cognition using functional magnetic resonance imaging (fMRI). Our results demonstrate the temporal connectivity of the dynamic large-scale creative networks between default mode network (DMN), salience network, and cerebellar network during creative cognition, and advance our understanding of the network neuroscience of creative cognition.

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