scholarly journals MEG source imaging detects optogenetically-induced activity in cortical and subcortical networks

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Gregory E. Alberto ◽  
Jennifer R. Stapleton-Kotloski ◽  
David C. Klorig ◽  
Emily R. Rogers ◽  
Christos Constantinidis ◽  
...  
Keyword(s):  
High Resolution ◽  
Brain Regions ◽  
Source Imaging ◽  
Magnetic Source Imaging ◽  
Functional Brain ◽  
Functional Brain Mapping ◽  
Functional Brain Networks ◽  
Intact Brain ◽  
Temporal Structures ◽  
Deep Sources

AbstractMagnetoencephalography measures neuromagnetic activity with high temporal, and theoretically, high spatial resolution. We developed an experimental platform combining MEG-compatible optogenetic techniques in nonhuman primates for use as a functional brain-mapping platform. Here we show localization of optogenetically evoked signals to known sources in the superficial arcuate sulcus of cortex and in CA3 of hippocampus at a resolution of 750 µm3. We detect activation in subcortical, thalamic, and extended temporal structures, conforming to known anatomical and functional brain networks associated with the respective sites of stimulation. This demonstrates that high-resolution localization of experimentally produced deep sources is possible within an intact brain. This approach is suitable for exploring causal relationships between discrete brain regions through precise optogenetic control and simultaneous whole brain MEG recording with high-resolution magnetic source imaging (MSI).

scholarly journals Optogenetic activation of nonhuman primate cortical and subcortical brain circuits highlights detection capabilities of MEG source imaging

2020 ◽  
Author(s):  
GE Alberto ◽  
JR Stapleton-Kotloski ◽  
DC Klorig ◽  
ER Rogers ◽  
C Constantinidis ◽  
...  
Keyword(s):  
Brain Regions ◽  
Temporal Networks ◽  
Source Imaging ◽  
Magnetic Source Imaging ◽  
Accurate Localization ◽  
Brain Circuits ◽  
Intact Brain ◽  
Detection Capabilities ◽  
Deep Sources ◽  
Highlights Detection

ABSTRACTMagnetoencephalography (MEG) measures neuromagnetic activity with high temporal, and theoretically, high spatial resolution. However, the ability of magnetic source imaging (MSI) to localize deep sources is uncertain. We developed an experimental platform combining MEG-compatible optogenetic techniques in non-human primates (NHPs) to test the ability of MEG/MSI to image deep signals. We demonstrate localization of optogenetically-evoked signals to known sources in the superficial arcuate sulcus of cortex and in CA3 of hippocampus at a resolution of 750 µm3. In response to stimulation of arcuate sulcus and hippocampus, we detected activation in subcortical and thalamic structures, or extended temporal networks, respectively. This is the first demonstration of accurate localization of deep sources within an intact brain using a novel combination of optogenetics with MEG/MSI. This approach is suitable for exploring causal relationships between discrete brain regions through precise optogenetic control and simultaneous whole brain recording.

Functional Brain Mapping During Surgery with Sensitive, High-resolution Digital Optical Infrared Imaging

Neurosurgery ◽  
1997 ◽  
Vol 41 (3) ◽  
pp. 751-751
Author(s):  
Gorbach Alexander ◽  
Heiss John ◽  
Kufta Conrad ◽  
H. Oldfield Edward
Keyword(s):  
High Resolution ◽  
Brain Mapping ◽  
Infrared Imaging ◽  
Functional Brain ◽  
Functional Brain Mapping

scholarly journals Unifying the Notions of Modularity and Core–Periphery Structure in Functional Brain Networks during Youth

2019 ◽  
Vol 30 (3) ◽  
pp. 1087-1102
Author(s):  
Shi Gu ◽  
Cedric Huchuan Xia ◽  
Rastko Ciric ◽  
Tyler M Moore ◽  
Ruben C Gur ◽  
...  
Keyword(s):  
Individual Differences ◽  
Brain Function ◽  
Brain Networks ◽  
Brain Regions ◽  
Imaging Data ◽  
Modular Architecture ◽  
Functional Brain ◽  
Rich Club ◽  
Functional Brain Networks ◽  
Integrative Processing

AbstractAt rest, human brain functional networks display striking modular architecture in which coherent clusters of brain regions are activated. The modular account of brain function is pervasive, reliable, and reproducible. Yet, a complementary perspective posits a core–periphery or rich-club account of brain function, where hubs are densely interconnected with one another, allowing for integrative processing. Unifying these two perspectives has remained difficult due to the fact that the methodological tools to identify modules are entirely distinct from the methodological tools to identify core–periphery structure. Here, we leverage a recently-developed model-based approach—the weighted stochastic block model—that simultaneously uncovers modular and core–periphery structure, and we apply it to functional magnetic resonance imaging data acquired at rest in 872 youth of the Philadelphia Neurodevelopmental Cohort. We demonstrate that functional brain networks display rich mesoscale organization beyond that sought by modularity maximization techniques. Moreover, we show that this mesoscale organization changes appreciably over the course of neurodevelopment, and that individual differences in this organization predict individual differences in cognition more accurately than module organization alone. Broadly, our study provides a unified assessment of modular and core–periphery structure in functional brain networks, offering novel insights into their development and implications for behavior.

The Oxford Handbook of Functional Brain Imaging in Neuropsychology and Cognitive Neurosciences

2014 ◽  
Keyword(s):  
Brain Imaging ◽  
Diffusion Tensor ◽  
Functional Neuroimaging ◽  
Functional Brain Imaging ◽  
Affective States ◽  
Source Imaging ◽  
Magnetic Source Imaging ◽  
Functional Brain ◽  
Positron Emission ◽  
Cognitive Neurosciences

A large part of the contemporary literature involves functional neuroimaging. Yet few readers are sufficiently familiar with the various imaging methods, their capabilities and limitations, to appraise it correctly. To fulfill that need is the purpose of this Handbook, which consists of an accessible description of the methods and their clinical and research applications. The Handbook begins with an overview of basic concepts of functional brain imaging, magnetoencephalography and the use of magnetic source imaging (MSI), positron emission tomography (PET), diffusion tensor imaging (DTI), and transcranial magnetic stimulation (TMS). The authors then discuss the various research applications of imaging, such as white matter connectivity; the function of the default mode network; the possibility and the utility of imaging of consciousness; the search for mnemonic traces of concepts the mechanisms of the encoding, consolidation, and retrieval of memories; executive functions and their neuroanatomical mechanisms; voluntary actions, human will and decision-making; motor cognition; language and the mechanisms of affective states and pain. The final chapter discusses the uses of functional neuroimaging in the presurgical mapping of the brain.

Functional brain imaging of language: criteria for scientific merit and supporting data from magnetic source imaging

2003 ◽  
Vol 16 (4-5) ◽  
pp. 255-275 ◽  
Author(s):  
Rebecca L Billingsley ◽  
Panagiotis G Simos ◽  
Eduardo M Castillo ◽  
Fernando Maestú ◽  
Shirin Sarkari ◽  
...  
Keyword(s):  
Brain Imaging ◽  
Functional Brain Imaging ◽  
Source Imaging ◽  
Magnetic Source Imaging ◽  
Functional Brain ◽  
Scientific Merit ◽  
Magnetic Source

scholarly journals Between-module functional connectivity of the salient ventral attention network and dorsal attention network is associated with motor inhibition

PLoS ONE ◽  
2020 ◽  
Vol 15 (12) ◽  
pp. e0242985
Author(s):  
Howard Muchen Hsu ◽  
Zai-Fu Yao ◽  
Kai Hwang ◽  
Shulan Hsieh
Keyword(s):  
Individual Differences ◽  
Brain Networks ◽  
Network Connectivity ◽  
Brain Regions ◽  
Motor Inhibition ◽  
Attention Network ◽  
Functional Brain ◽  
Graph Theoretic ◽  
Dorsal Attention Network ◽  
Functional Brain Networks

The ability to inhibit motor response is crucial for daily activities. However, whether brain networks connecting spatially distinct brain regions can explain individual differences in motor inhibition is not known. Therefore, we took a graph-theoretic perspective to examine the relationship between the properties of topological organization in functional brain networks and motor inhibition. We analyzed data from 141 healthy adults aged 20 to 78, who underwent resting-state functional magnetic resonance imaging and performed a stop-signal task along with neuropsychological assessments outside the scanner. The graph-theoretic properties of 17 functional brain networks were estimated, including within-network connectivity and between-network connectivity. We employed multiple linear regression to examine how these graph-theoretical properties were associated with motor inhibition. The results showed that between-network connectivity of the salient ventral attention network and dorsal attention network explained the highest and second highest variance of individual differences in motor inhibition. In addition, we also found those two networks span over brain regions in the frontal-cingulate-parietal network, suggesting that these network interactions are also important to motor inhibition.

scholarly journals Synchrony and complexity in state-related EEG networks: an application of spectral graph theory

2019 ◽  
Author(s):  
Amir Hossein Ghaderi ◽  
Bianca R. Baltaretu ◽  
Masood Nemati Andevari ◽  
Vishal Bharmauria ◽  
Fuat Balci
Keyword(s):  
Graph Theory ◽  
Brain Networks ◽  
Brain Regions ◽  
Brain Network ◽  
Spectral Graph Theory ◽  
Theory Approach ◽  
Topological Properties ◽  
Functional Brain ◽  
Spectral Graph ◽  
Functional Brain Networks

AbstractTo characterize differences between different state-related brain networks, statistical graph theory approaches have been employed to identify informative, topological properties. However, dynamical properties have been studied little in this regard. Our goal here was to introduce spectral graph theory as a reliable approach to determine dynamic properties of functional brain networks and to find how topological versus dynamical features differentiate between such networks. To this goal, 45 participants performed no task with eyes open (EO) or closed (EC) while electroencephalography data were recorded. These data were used to create weighted adjacency matrices for each condition (rest state EO and rest state EC). Then, using the spectral graph theory approach and Shannon entropy, we identified dynamical properties for weighted graphs, and we compared these features with topological aspects of graphs. The results showed that spectral graph theory can distinguish different state-dependent neural networks with different synchronies. On the other hand, correlation analysis indicated that although dynamical and topological properties of random networks are completely independent, these network features can be related in the case of brain generated graphs. In conclusion, the spectral graph theory approach can be used to make inferences about various state-related brain networks, for healthy and clinical populations.Author SummeryBy considering functional communications across different brain regions, a complex network is achieved that is known as functional brain network. Topologically, this network is constructed by different nodes (activity of brain regions or signals over recording electrodes) and different edges (similarity, correlation or phase difference between nodes). Paths, clusters, hubs, and centrality of nodes are examples of topological properties of these networks. However, synchrony and stability of functional brain networks can not be revealed by consideration of topological properties. Alternatively, spectral graph theory (SGT) can demonstrate the dynamic, synchrony and stability of graphs. But this approach has been studied little in brain network analysis. Here, we employed SGT, as well as topological methods, to investigate which approaches are more reliable to find differences between distinct state-related brain networks. On the other hand, we investigated correlations between topology and dynamic in different type of networks (brain generated and random networks). We found that SGT measures can clearly distinguish between distinct state-related brain networks and it can reveal synchrony and complexity of these networks. Also, results show that although dynamic and topology of random-generated graph are completely independent, these properties exhibit several correlations in the case of functional brain networks.

scholarly journals ADHD Symptoms are Associated with the Modular Structure of Intrinsic Brain Networks in a Representative Sample of Healthy Adults

2018 ◽  
Author(s):  
Kirsten Hilger ◽  
Christian J. Fiebach
Keyword(s):  
Representative Sample ◽  
Information Transfer ◽  
Adult Population ◽  
Brain Networks ◽  
Brain Regions ◽  
Adhd Symptoms ◽  
Functional Brain ◽  
Default Mode ◽  
Functional Brain Networks ◽  
And Task

AbstractAttention-deficit/hyperactivity disorder (ADHD) is one of the most common neurodevelopmental disorders with significant and often lifelong effects on social, emotional, and cognitive functioning. Influential neurocognitive models of ADHD link behavioral symptoms to altered connections between and within functional brain networks. Here, we investigate whether network-based theories of ADHD can be generalized to understanding variations in ADHD-related behaviors within the normal (i.e., clinically unaffected) adult population. In a large and representative sample, self-rated presence of ADHD symptoms varied widely; only eight out of 291 participants scored in the clinical range. Subject-specific brain-network graphs were modeled from functional MRI resting-state data and revealed significant associations between (non-clinical) ADHD symptoms and region-specific profiles of between-module and within-module connectivity. Effects were located in brain regions associated with multiple neuronal systems including the default-mode network, the salience network, and the central executive system. Our results are consistent with network perspectives of ADHD and provide further evidence for the relevance of an appropriate information transfer between task-negative (default-mode) and task-positive brain regions. More generally, our findings support a dimensional conceptualization of ADHD and contribute to a growing understanding of cognition as an emerging property of functional brain networks.Author SummaryNeurocognitive models of ADHD link behavioral symptoms to altered connections between and within functional brain networks. We investigate whether these network-based theories of ADHD can be generalized to ADHD-related behaviors within the normal adult population. Subject-specific brain graphs were modeled from functional MRI resting-state data of a large and representative sample (N = 291). Significant associations between ADHD-related behaviors and region-specific profiles of between-module and within-module connectivity were observed in brain regions associated with multiple functional systems including the default-mode network, the salience network, and the central executive system. Our results support a dimensional conceptualization of ADHD and enforce network-based models of ADHD by providing further evidence for the relevance of an appropriate information transfer between task-negative (default-mode) and task-positive brain regions.

Sign in / Sign up

Export Citation Format

Share Document