scholarly journals Time varying connectivity across the brain changes as a function of nicotine abstinence state

2020 ◽  
Author(s):  
John R. Fedota ◽  
Thomas J. Ross ◽  
Juan Castillo ◽  
Michael R. McKenna ◽  
Allison L. Matous ◽  
...  
Keyword(s):  
Large Scale ◽  
Clinical Symptoms ◽  
Network Communication ◽  
Time Varying ◽  
Resting State Functional Connectivity ◽  
Nicotine Abstinence ◽  
Temporal Flexibility ◽  
Community Boundaries ◽  
Scale Network ◽  
The Brain

ABSTRACTBackgroundThe Nicotine Withdrawal Syndrome (NWS) includes affective and cognitive disruptions whose incidence and severity vary across time during acute abstinence. However, most network-level neuroimaging employs static measures of resting state functional connectivity (rsFC), assuming time-invariance, and unable to capture dynamic brain-behavior relationships. Recent advances in rsFC signal processing allow characterization of “time varying connectivity” (TVC), which characterizes network communication between sub-networks that reconfigure over the course of data collection. As such, TVC may more fully describe network dysfunction related to the NWS.MethodsTo isolate alterations in the frequency and diversity of communication across network boundaries as a function of acute nicotine abstinence we scanned cigarette smokers in the nicotine sated and abstinent states and applied a previously-validated method to characterize TVC at a network and nodal level within the brain.ResultsDuring abstinence, we found brain wide decreases in the frequency of interactions between network nodes in different modular communities (i.e. temporal flexibility; TF). In addition, within a subset of the networks examined the variability of these interactions across community boundaries (i.e. spatiotemporal diversity; STD) also decreased. Finally, within two of these networks the decrease in STD was significantly related to NWS clinical symptoms.ConclusionsEmploying multiple measures of TVC in a within subjects’ design, we characterized a novel set of changes in network communication and link these changes to specific behavioral symptoms of the NWS. These reductions in TVC provide a meso-scale network description of the relative inflexibility of specific large-scale brain networks as a result of acute abstinence.

scholarly journals Go with the FLOW: visualizing spatiotemporal dynamics in optical widefield calcium imaging

2021 ◽  
Vol 18 (181) ◽  
pp. 20210523
Author(s):  
Nathaniel J. Linden ◽  
Dennis R. Tabuena ◽  
Nicholas A. Steinmetz ◽  
William J. Moody ◽  
Steven L. Brunton ◽  
...  
Keyword(s):  
Coherent Structures ◽  
Calcium Imaging ◽  
Large Scale ◽  
Vector Fields ◽  
Brain Activity ◽  
Time Varying ◽  
Finite Time Lyapunov Exponent ◽  
Synthetic Datasets ◽  
The Brain ◽  
Analyse Time

Widefield calcium imaging has recently emerged as a powerful experimental technique to record coordinated large-scale brain activity. These measurements present a unique opportunity to characterize spatiotemporally coherent structures that underlie neural activity across many regions of the brain. In this work, we leverage analytic techniques from fluid dynamics to develop a visualization framework that highlights features of flow across the cortex, mapping wavefronts that may be correlated with behavioural events. First, we transform the time series of widefield calcium images into time-varying vector fields using optic flow. Next, we extract concise diagrams summarizing the dynamics, which we refer to as FLOW (flow lines in optical widefield imaging) portraits . These FLOW portraits provide an intuitive map of dynamic calcium activity, including regions of initiation and termination, as well as the direction and extent of activity spread. To extract these structures, we use the finite-time Lyapunov exponent technique developed to analyse time-varying manifolds in unsteady fluids. Importantly, our approach captures coherent structures that are poorly represented by traditional modal decomposition techniques. We demonstrate the application of FLOW portraits on three simple synthetic datasets and two widefield calcium imaging datasets, including cortical waves in the developing mouse and spontaneous cortical activity in an adult mouse.

scholarly journals Synthesizing Brain-network-inspired Interconnections for Large-scale Network-on-chips

2022 ◽  
Vol 27 (1) ◽  
pp. 1-30
Author(s):  
Mengke Ge ◽  
Xiaobing Ni ◽  
Xu Qi ◽  
Song Chen ◽  
Jinglei Huang ◽  
...  
Keyword(s):  
Large Scale ◽  
Small World ◽  
Brain Network ◽  
Graph Processing ◽  
Hop Count ◽  
Scale Free ◽  
Large Scale Network ◽  
Average Latency ◽  
Scale Network ◽  
The Brain

Brain network is a large-scale complex network with scale-free, small-world, and modularity properties, which largely supports this high-efficiency massive system. In this article, we propose to synthesize brain-network-inspired interconnections for large-scale network-on-chips. First, we propose a method to generate brain-network-inspired topologies with limited scale-free and power-law small-world properties, which have a low total link length and extremely low average hop count approximately proportional to the logarithm of the network size. In addition, given the large-scale applications, considering the modularity of the brain-network-inspired topologies, we present an application mapping method, including task mapping and deterministic deadlock-free routing, to minimize the power consumption and hop count. Finally, a cycle-accurate simulator BookSim2 is used to validate the architecture performance with different synthetic traffic patterns and large-scale test cases, including real-world communication networks for the graph processing application. Experiments show that, compared with other topologies and methods, the brain-network-inspired network-on-chips (NoCs) generated by the proposed method present significantly lower average hop count and lower average latency. Especially in graph processing applications with a power-law and tightly coupled inter-core communication, the brain-network-inspired NoC has up to 70% lower average hop count and 75% lower average latency than mesh-based NoCs.

scholarly journals Resting-state functional connectivity and quantitation of glutamate and GABA of the PCC/precuneus by magnetic resonance spectroscopy at 7T in healthy individuals

PLoS ONE ◽  
2020 ◽  
Vol 15 (12) ◽  
pp. e0244491
Author(s):  
Ofer M. Gonen ◽  
Bradford A. Moffat ◽  
Patrick Kwan ◽  
Terence J. O’Brien ◽  
Patricia M. Desmond ◽  
...  
Keyword(s):  
Magnetic Resonance ◽  
Functional Connectivity ◽  
Magnetic Resonance Spectroscopy ◽  
Large Scale ◽  
Resonance Spectroscopy ◽  
Resting State Functional Connectivity ◽  
Disease States ◽  
Large Scale Network ◽  
Age Related ◽  
Scale Network

The default mode network (DMN) is the main large-scale network of the resting brain and the PCC/precuneus is a major hub of this network. Glutamate and GABA (γ-amino butyric acid) are the main excitatory and inhibitory neurotransmitters in the CNS, respectively. We studied glutamate and GABA concentrations in the PCC/precuneus via magnetic resonance spectroscopy (MRS) at 7T in relation to age and correlated them with functional connectivity between this region and other DMN nodes in ten healthy right-handed volunteers ranging in age between 23–68 years. Mean functional connectivity of the PCC/precuneus to the other DMN nodes and the glutamate/GABA ratio significantly correlated with age (r = 0.802, p = 0.005 and r = 0.793, p = 0.006, respectively) but not with each other. Glutamate and GABA alone did not significantly correlate with age nor with functional connectivity within the DMN. The glutamate/GABA ratio and functional connectivity of the PCC/precuneus are, therefore, independent age-related biomarkers of the DMN and may be combined in a multimodal pipeline to study DMN alterations in various disease states.

scholarly journals Delineating visual, auditory and motor regions in the human brain with functional neuroimaging: a BrainMap-based meta-analytic synthesis

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Marisa K. Heckner ◽  
Edna C. Cieslik ◽  
Vincent Küppers ◽  
Peter T. Fox ◽  
Simon B. Eickhoff ◽  
...  
Keyword(s):  
Large Scale ◽  
Functional Neuroimaging ◽  
Motor Task ◽  
Brain Regions ◽  
Affective Processing ◽  
Resting State Functional Connectivity ◽  
Motor Processes ◽  
Brain Correlates ◽  
Meta Analyses ◽  
The Brain

AbstractMost everyday behaviors and laboratory tasks rely on visual, auditory and/or motor-related processes. Yet, to date, there has been no large-scale quantitative synthesis of functional neuroimaging studies mapping the brain regions consistently recruited during such perceptuo-motor processing. We therefore performed three coordinate-based meta-analyses, sampling the results of neuroimaging experiments on visual (n = 114), auditory (n = 122), or motor-related (n = 251) processing, respectively, from the BrainMap database. Our analyses yielded both regions known to be recruited for basic perceptual or motor processes and additional regions in posterior frontal cortex. Comparing our results with data-driven network definitions based on resting-state functional connectivity revealed good overlap in expected regions but also showed that perceptual and motor task-related activations consistently involve additional frontal, cerebellar, and subcortical areas associated with “higher-order” cognitive functions, extending beyond what is captured when the brain is at “rest.” Our resulting sets of domain-typical brain regions can be used by the neuroimaging community as robust functional definitions or masks of regions of interest when investigating brain correlates of perceptual or motor processes and their interplay with other mental functions such as cognitive control or affective processing. The maps are made publicly available via the ANIMA database.

scholarly journals A macaque connectome for large-scale network simulations in TheVirtualBrain

2018 ◽  
Author(s):  
Kelly Shen ◽  
Gleb Bezgin ◽  
Michael Schirner ◽  
Petra Ritter ◽  
Stefan Everling ◽  
...  
Keyword(s):  
Resting State ◽  
Brain Function ◽  
Large Scale ◽  
Spatial And Temporal Scales ◽  
Multi Scale ◽  
Large Scale Network ◽  
Scale Network ◽  
Network Simulations ◽  
Dynamical Function ◽  
The Brain

AbstractModels of large-scale brain networks that are informed by the underlying anatomical connectivity contribute to our understanding of the mapping between the structure of the brain and its dynamical function. Connectome-based modelling is a promising approach to a more comprehensive understanding of brain function across spatial and temporal scales, but it must be constrained by multi-scale empirical data from animal models. Here we describe the construction of a macaque connectome for whole-cortex simulations in TheVirtualBrain, an open-source simulation platform. We take advantage of available axonal tract-tracing datasets and enhance the existing connectome data using diffusion-based tractography in macaques. We illustrate the utility of the connectome as an extension of TheVirtualBrain by simulating resting-state BOLD-fMRI data and fitting it to empirical resting-state data.

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