scholarly journals A novel dynamic network imaging analysis method reveals aging-related fragmentation of cortical networks in mouse

2021 ◽  
pp. 1-28
Author(s):  
Daniel A Llano ◽  
Chihua Ma ◽  
Umberto Di Fabrizio ◽  
Aynaz Taheri ◽  
Kevin A. Stebbings ◽  
...  
Keyword(s):  
Large Scale ◽  
Brain Slices ◽  
Dynamic Network ◽  
Network Connectivity ◽  
Analytical Tool ◽  
Autofluorescence Imaging ◽  
Imaging Data ◽  
Cortical Networks ◽  
Network Metrics

Abstract Network analysis of large-scale neuroimaging data is a particularly challenging computational problem. Here, we adapt a novel analytical tool, the community dynamic inference method (CommDy), for brain imaging data from young and aged mice. CommDy, which was inspired by social network theory, has been successfully used in other domains in biology; this report represents its first use in neuroscience. We used CommDy to investigate aging-related changes in network metrics in the auditory and motor cortices using flavoprotein autofluorescence imaging in brain slices and in vivo. We observed that auditory cortical networks in slices taken from aged brains were highly fragmented compared to networks observed in young animals. CommDy network metrics were then used to build a random-forests classifier based on NMDA-receptor blockade data, which successfully reproduced the aging findings, suggesting that the excitatory cortical connections may be altered during aging. A similar aging-related decline in network connectivity was also observed in spontaneous activity in the awake motor cortex, suggesting that the findings in the auditory cortex reflect general mechanisms during aging. These data suggest that CommDy provides a new dynamic network analytical tool to study the brain and that aging is associated with fragmentation of intracortical networks.

scholarly journals A novel dynamic network imaging analysis method reveals aging-related fragmentation of cortical networks in mouse

2019 ◽  
Author(s):  
Daniel A Llano ◽  
Chihua Ma ◽  
Umberto Di Fabrizio ◽  
Aynaz Taheri ◽  
Kevin A. Stebbings ◽  
...  
Keyword(s):  
Auditory Cortex ◽  
Large Scale ◽  
Brain Slices ◽  
Dynamic Network ◽  
Analytical Tool ◽  
Aging Brain ◽  
Autofluorescence Imaging ◽  
Imaging Data ◽  
Cortical Networks ◽  
Network Metrics

AbstractNetwork analysis of large-scale neuroimaging data has proven to be a particularly challenging computational problem. In this study, we adapt a novel analytical tool, known as the community dynamic inference method (CommDy), which was inspired by social network theory, for the study of brain imaging data from an aging mouse model. CommDy has been successfully used in other domains in biology; this report represents its first use in neuroscience. We used CommDy to investigate aging-related changes in network parameters in the auditory and motor cortices using flavoprotein autofluorescence imaging in brain slices and in vivo. Analysis of spontaneous activations in the auditory cortex of slices taken from young and aged animals demonstrated that cortical networks in aged brains were highly fragmented compared to networks observed in young animals. Specifically, the degree of connectivity of each activated node in the aged brains was significantly lower than those seen in the young brain, and multivariate analyses of all derived network metrics showed distinct clusters of these metrics in young vs. aged brains. CommDy network metrics were then used to build a random-forests classifier based on NMDA-receptor blockade data, which successfully recapitulated the aging findings, suggesting that the excitatory synaptic substructure of the auditory cortex may be altered during aging. A similar aging-related decline in network connectivity was also observed in spontaneous activity obtained from the awake motor cortex, suggesting that the findings in the auditory cortex are reflections of general mechanisms that occur during aging. Therefore, CommDy therefore provides a new dynamic network analytical tool to study the brain and provides links between network-level and synaptic-level dysfunction in the aging brain.

scholarly journals Inference of Synaptic Connectivity and External Variability in Neural Microcircuits

2019 ◽  
Author(s):  
Cody Baker ◽  
Emmanouil Froudarakis ◽  
Dimitri Yatsenko ◽  
Andreas S. Tolias ◽  
Robert Rosenbaum
Keyword(s):  
Functional Connectivity ◽  
Large Scale ◽  
Network Models ◽  
Ground Truth ◽  
External Input ◽  
Synaptic Connectivity ◽  
Imaging Data ◽  
Common Input ◽  
Simplifying Assumptions

AbstractA major goal in neuroscience is to estimate neural connectivity from large scale extracellular recordings of neural activity in vivo. This is challenging in part because any such activity is modulated by the unmeasured external synaptic input to the network, known as the common input problem. Many different measures of functional connectivity have been proposed in the literature, but their direct relationship to synaptic connectivity is often assumed or ignored. For in vivo data, measurements of this relationship would require a knowledge of ground truth connectivity, which is nearly always unavailable. Instead, many studies use in silico simulations as benchmarks for investigation, but such approaches necessarily rely upon a variety of simplifying assumptions about the simulated network and can depend on numerous simulation parameters. We combine neuronal network simulations, mathematical analysis, and calcium imaging data to address the question of when and how functional connectivity, synaptic connectivity, and latent external input variability can be untangled. We show numerically and analytically that, even though the precision matrix of recorded spiking activity does not uniquely determine synaptic connectivity, it is often closely related to synaptic connectivity in practice under various network models. This relation becomes more pronounced when the spatial structure of neuronal variability is considered jointly with precision.

scholarly journals Opportunities for Understanding MS Mechanisms and Progression With MRI Using Large-Scale Data Sharing and Artificial Intelligence

Neurology ◽  
2021 ◽  
pp. 10.1212/WNL.0000000000012884
Author(s):  
Hugo Vrenken ◽  
Mark Jenkinson ◽  
Dzung Pham ◽  
Charles R.G. Guttmann ◽  
Deborah Pareto ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Image Analysis ◽  
Data Sharing ◽  
Large Scale ◽  
Personal Data ◽  
Human Observer ◽  
Imaging Data ◽  
Large Scale Data ◽  
Scale Data

Multiple sclerosis (MS) patients have heterogeneous clinical presentations, symptoms and progression over time, making MS difficult to assess and comprehend in vivo. The combination of large-scale data-sharing and artificial intelligence creates new opportunities for monitoring and understanding MS using magnetic resonance imaging (MRI).First, development of validated MS-specific image analysis methods can be boosted by verified reference, test and benchmark imaging data. Using detailed expert annotations, artificial intelligence algorithms can be trained on such MS-specific data. Second, understanding disease processes could be greatly advanced through shared data of large MS cohorts with clinical, demographic and treatment information. Relevant patterns in such data that may be imperceptible to a human observer could be detected through artificial intelligence techniques. This applies from image analysis (lesions, atrophy or functional network changes) to large multi-domain datasets (imaging, cognition, clinical disability, genetics, etc.).After reviewing data-sharing and artificial intelligence, this paper highlights three areas that offer strong opportunities for making advances in the next few years: crowdsourcing, personal data protection, and organized analysis challenges. Difficulties as well as specific recommendations to overcome them are discussed, in order to best leverage data sharing and artificial intelligence to improve image analysis, imaging and the understanding of MS.

scholarly journals Multivariate associative patterns between the gut microbiota and large-scale brain network connectivity

2020 ◽  
Author(s):  
N. Kohn ◽  
J. Szopinska-Tokov ◽  
A. Llera ◽  
C. Beckmann ◽  
A. Arias Vasquez ◽  
...  
Keyword(s):  
Gut Microbiota ◽  
Default Mode Network ◽  
Large Scale ◽  
Network Connectivity ◽  
Brain Network ◽  
Imaging Data ◽  
Microbial Composition ◽  
Attention Networks ◽  
Default Mode ◽  
Brain Network Connectivity

AbstractResearch on the gut-brain axis has accelerated substantially over the course of the last years. Many reviews have outlined the important implications of understanding the relation of the gut microbiota with human brain function and behavior. One substantial drawback in integrating gut microbiome and brain data is the lack of integrative multivariate approaches that enable capturing variance in both modalities simultaneously. To address this issue, we applied a linked independent component analysis (LICA) to microbiota and brain connectivity data.We analyzed data from 58 healthy females (mean age = 21.5 years). Magnetic Resonance Imaging data were acquired using resting state functional imaging data. The assessment of gut microbial composition from feces was based on sequencing of the V4 16S rRNA gene region. We used the LICA model to simultaneously factorize the subjects’ large-scale brain networks and microbiome relative abundance data into 10 independent components of spatial and abundance variation.LICA decomposition resulted in four components with non-marginal contribution of the microbiota data. The default mode network featured strongly in three components, whereas the two-lateralized fronto-parietal attention networks contributed to one component. The executive-control (with the default mode) network was associated to another component. We found the abundance of Prevotella genus was associated to the strength of expression of all networks, whereas Bifidobacterium was associated with the default mode and frontoparietal-attention networks.We provide the first exploratory evidence for multivariate associative patterns between the gut microbiota and brain network connectivity in healthy humans, taking into account the complexity of both systems.

scholarly journals OnACID: Online Analysis of Calcium Imaging Data in Real Time*

2017 ◽  
Author(s):  
Andrea Giovannucci ◽  
Johannes Friedrich ◽  
Matt Kaufman ◽  
Anne Churchland ◽  
Dmitri Chklovskii ◽  
...  
Keyword(s):  
Real Time ◽  
Calcium Imaging ◽  
Large Scale ◽  
Motion Artifact ◽  
Imaging Data ◽  
Neuronal Populations ◽  
Calcium Indicators ◽  
Online Dictionary Learning ◽  
Recorded Data

AbstractOptical imaging methods using calcium indicators are critical for monitoring the activity of large neuronal populations in vivo. Imaging experiments typically generate a large amount of data that needs to be processed to extract the activity of the imaged neuronal sources. While deriving such processing algorithms is an active area of research, most existing methods require the processing of large amounts of data at a time, rendering them vulnerable to the volume of the recorded data, and preventing realtime experimental interrogation. Here we introduce OnACID, an Online framework for the Analysis of streaming Calcium Imaging Data, including i) motion artifact correction, ii) neuronal source extraction, and iii) activity denoising and deconvolution. Our approach combines and extends previous work on online dictionary learning and calcium imaging data analysis, to deliver an automated pipeline that can discover and track the activity of hundreds of cells in real time, thereby enabling new types of closed-loop experiments. We apply our algorithm on two large scale experimental datasets, benchmark its performance on manually annotated data, and show that it outperforms a popular offline approach.

scholarly journals Long-range order from local interactions: organization and development of distributed cortical networks

2018 ◽  
Author(s):  
Gordon B. Smith ◽  
Bettina Hein ◽  
David E. Whitney ◽  
David Fitzpatrick ◽  
Matthias Kaschube
Keyword(s):  
Visual Cortex ◽  
Spontaneous Activity ◽  
Long Range ◽  
Network Structure ◽  
Large Scale ◽  
Wide Field ◽  
Cortical Networks ◽  
Subsequent Formation ◽  
Correlated Activity

The cortical networks that underlie behavior exhibit an orderly functional organization at local and global scales, which is readily evident in the visual cortex of carnivores and primates1-6. Here, neighboring columns of neurons represent the full range of stimulus orientations and contribute to distributed networks spanning several millimeters2,7-11. However, the principles governing functional interactions that bridge this fine-scale functional architecture and distant network elements are unclear, and the emergence of these network interactions during development remains unexplored. Here, by using in vivo wide-field and 2-photon calcium imaging of spontaneous activity patterns in mature ferret visual cortex, we find widespread and specific modular correlation patterns that accurately predict the local structure of visually-evoked orientation columns from the spontaneous activity of neurons that lie several millimeters away. The large-scale networks revealed by correlated spontaneous activity show abrupt ‘fractures’ in continuity that are in tight register with evoked orientation pinwheels. Chronic in vivo imaging demonstrates that these large-scale modular correlation patterns and fractures are already present at early stages of cortical development and predictive of the mature network structure. Silencing feed-forward drive through either retinal or thalamic blockade does not affect network structure suggesting a cortical origin for this large-scale correlated activity, despite the immaturity of long-range horizontal network connections in the early cortex. Using a circuit model containing only local connections, we demonstrate that such a circuit is sufficient to generate large-scale correlated activity, while also producing correlated networks showing strong fractures, a reduced dimensionality, and an elongated local correlation structure, all in close agreement with our empirical data. These results demonstrate the precise local and global organization of cortical networks revealed through correlated spontaneous activity and suggest that local connections in early cortical circuits may generate structured long-range network correlations that underlie the subsequent formation of visually-evoked distributed functional networks.

Voltage-Sensitive Dye Imaging

2015 ◽  
Author(s):  
William Frost ◽  
Jian-young Wu
Keyword(s):  
Large Scale ◽  
Brain Slices ◽  
Network Activity ◽  
Neural Signals ◽  
Voltage Sensitive Dye ◽  
Large Scale Network ◽  
Scale Network ◽  
Invertebrate Ganglia ◽  
Voltage Sensitive Dye Imaging

Voltage sensitive dye imaging (VSD) can be used to record neural activity in hundreds of locations in preparations ranging from mammalian cortex to invertebrate ganglia. Because fast VSDs respond to membrane potential changes with microsecond temporal resolution, these are better suited than calcium indicators for recording rapid neural signals. Here we describe methods for using a 464- element photodiode array and fast VSDs to record signals ranging from large scale network activity in brain slices and in vivo mammalian preparations, to action potentials in over 100 individual neurons in invertebrate ganglia.

scholarly journals Determining the Hierarchical Architecture of the Human Brain Using Subject-Level Clustering of Functional Networks

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Teddy J. Akiki ◽  
Chadi G. Abdallah
Keyword(s):  
Large Scale ◽  
Community Organization ◽  
Hierarchical Architecture ◽  
Imaging Data ◽  
Large Scale Network ◽  
Human Connectome Project ◽  
Neuronal Connections ◽  
Scale Network ◽  
Cortical Regions

AbstractOptimal integration and segregation of neuronal connections are necessary for efficient large-scale network communication between distributed cortical regions while allowing for modular specialization. This dynamic in the cortex is enabled at the network mesoscale by the organization of nodes into communities. Previous in vivo efforts to map the mesoscale architecture in humans had several limitations. Here we characterize a consensus multiscale community organization of the functional cortical network. We derive this consensus from the clustering of subject-level networks. We applied this analysis to magnetic resonance imaging data from 1003 healthy individuals part of the Human Connectome Project. The hierarchical atlas and code will be made publicly available for future investigators.

Flavoprotein Autofluorescence Imaging of Neuronal Activation in the Cerebellar Cortex In Vivo

2004 ◽  
Vol 92 (1) ◽  
pp. 199-211 ◽  
Author(s):  
Kenneth C. Reinert ◽  
Robert L. Dunbar ◽  
Wangcai Gao ◽  
Gang Chen ◽  
Timothy J. Ebner
Keyword(s):  
Neuronal Activity ◽  
Cerebellar Cortex ◽  
Brain Slices ◽  
Optical Signal ◽  
Mitochondrial Metabolism ◽  
Autofluorescence Imaging ◽  
Transverse Beam ◽  
Electrophysiological Response ◽  
Indirect Measure

Autofluorescence has been used as an indirect measure of neuronal activity in isolated cell cultures and brain slices, but only to a limited extent in vivo. Intrinsic fluorescence signals reflect the coupling between neuronal activity and mitochondrial metabolism, and are caused by the oxidation/reduction of flavoproteins or nicotinamide adenine dinucleotide (NADH). The present study evaluated the existence and properties of these autofluorescence signals in the cerebellar cortex of the ketamine/xylazine anesthetized mouse in vivo. Surface stimulation of the unstained cerebellar cortex evoked a narrow, transverse beam of optical activity consisting of a large amplitude, short latency increase in fluorescence followed by a longer duration decrease. The optimal wavelengths for this autofluorescence signal were 420–490 nm for excitation and 515–570 nm for emission, consistent with a flavoprotein origin. The amplitude of the optical signal was linearly related to stimulation amplitude and frequency, and its duration was linearly related to the duration of stimulation. Blocking synaptic transmission demonstrated that a majority of the autofluorescence signal is attributed to activating the postsynaptic targets of the parallel fibers. Hypothesized to be the result of oxidation and subsequent reduction of flavoproteins, blocking mitochondrial respiration with sodium cyanide or inactivation of flavoproteins with diphenyleneiodonium substantially reduced the optical signal. This reduction in the autofluorescence signal was accomplished without altering the presynaptic and postsynaptic components of the electrophysiological response. Results from reflectance imaging and blocking nitric oxide synthase demonstrated that the epifluorescence signal is not the result of changes in hemoglobin oxygenation or blood flow. This flavoprotein autofluorescence signal thus provides a powerful tool to monitor neuronal activity in vivo and its relationship to mitochondrial metabolism.

scholarly journals Intraoperative Brain Mapping by Cortico-Cortical Evoked Potential

2021 ◽  
Vol 15 ◽  
Author(s):  
Yukihiro Yamao ◽  
Riki Matsumoto ◽  
Takayuki Kikuchi ◽  
Kazumichi Yoshida ◽  
Takeharu Kunieda ◽  
...  
Keyword(s):  
Real Time ◽  
Brain Function ◽  
Large Scale ◽  
Single Pulse ◽  
Connectivity Pattern ◽  
Cortical Networks ◽  
Temporal Lobes ◽  
Intraoperative Electrophysiological Monitoring ◽  
And Function

To preserve postoperative brain function, it is important for neurosurgeons to fully understand the brain's structure, vasculature, and function. Intraoperative high-frequency electrical stimulation during awake craniotomy is the gold standard for mapping the function of the cortices and white matter; however, this method can only map the “focal” functions and cannot monitor large-scale cortical networks in real-time. Recently, an in vivo electrophysiological method using cortico-cortical evoked potentials (CCEPs) induced by single-pulse electrical cortical stimulation has been developed in an extraoperative setting. By using the CCEP connectivity pattern intraoperatively, mapping and real-time monitoring of the dorsal language pathway is available. This intraoperative CCEP method also allows for mapping of the frontal aslant tract, another language pathway, and detection of connectivity between the primary and supplementary motor areas in the frontal lobe network. Intraoperative CCEP mapping has also demonstrated connectivity between the frontal and temporal lobes, likely via the ventral language pathway. Establishing intraoperative electrophysiological monitoring is clinically useful for preserving brain function, even under general anesthesia. This CCEP technique demonstrates potential clinical applications for mapping and monitoring large-scale cortical networks.

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