scholarly journals Transcriptional profiles of supragranular-enriched genes associate with corticocortical network architecture in the human brain

2016 ◽  
Vol 113 (4) ◽  
pp. E469-E478 ◽  
Author(s):  
Fenna M. Krienen ◽  
B. T. Thomas Yeo ◽  
Tian Ge ◽  
Randy L. Buckner ◽  
Chet C. Sherwood
Keyword(s):  
Cerebral Cortex ◽  
Human Brain ◽  
Long Range ◽  
Network Architecture ◽  
Large Scale ◽  
Nonhuman Primates ◽  
Brain Network ◽  
Molecular Architecture ◽  
Gene Sets ◽  
Molecular Profiles

The human brain is patterned with disproportionately large, distributed cerebral networks that connect multiple association zones in the frontal, temporal, and parietal lobes. The expansion of the cortical surface, along with the emergence of long-range connectivity networks, may be reflected in changes to the underlying molecular architecture. Using the Allen Institute’s human brain transcriptional atlas, we demonstrate that genes particularly enriched in supragranular layers of the human cerebral cortex relative to mouse distinguish major cortical classes. The topography of transcriptional expression reflects large-scale brain network organization consistent with estimates from functional connectivity MRI and anatomical tracing in nonhuman primates. Microarray expression data for genes preferentially expressed in human upper layers (II/III), but enriched only in lower layers (V/VI) of mouse, were cross-correlated to identify molecular profiles across the cerebral cortex of postmortem human brains (n = 6). Unimodal sensory and motor zones have similar molecular profiles, despite being distributed across the cortical mantle. Sensory/motor profiles were anticorrelated with paralimbic and certain distributed association network profiles. Tests of alternative gene sets did not consistently distinguish sensory and motor regions from paralimbic and association regions: (i) genes enriched in supragranular layers in both humans and mice, (ii) genes cortically enriched in humans relative to nonhuman primates, (iii) genes related to connectivity in rodents, (iv) genes associated with human and mouse connectivity, and (v) 1,454 gene sets curated from known gene ontologies. Molecular innovations of upper cortical layers may be an important component in the evolution of long-range corticocortical projections.

scholarly journals Implantable computer-controlled adaptive multielectrode positioning system

2018 ◽  
Vol 119 (4) ◽  
pp. 1471-1484 ◽  
Author(s):  
E. Ferrea ◽  
L. Suriya-Arunroj ◽  
D. Hoehl ◽  
U. Thomas ◽  
A. Gail
Keyword(s):  
Cerebral Cortex ◽  
Large Scale ◽  
Nonhuman Primates ◽  
Tissue Response ◽  
High Signal ◽  
Signal Quality ◽  
Positioning System ◽  
Multielectrode Arrays ◽  
User Friendliness ◽  
Computer Controlled

Acute neuronal recordings performed with metal microelectrodes in nonhuman primates allow investigating the neural substrate of complex cognitive behaviors. Yet the daily reinsertion and positioning of the electrodes prevents recording from many neurons simultaneously, limiting the suitability of these types of recordings for brain-computer interface applications or for large-scale population statistical methods on a trial-by-trial basis. In contrast, chronically implanted multielectrode arrays offer the opportunity to record from many neurons simultaneously, but immovable electrodes prevent optimization of the signal during and after implantation and cause the tissue response to progressively impair the transduced signal quality, thereby limiting the number of different neurons that can be recorded over the lifetime of the implant. Semichronically implanted matrices of electrodes, instead, allow individually movable electrodes in depth and achieve higher channel count compared with acute methods, hence partially overcoming these limitations. Existing semichronic systems with higher channel count lack computerized control of electrode movements, leading to limited user-friendliness and uncertainty in depth positioning. Here we demonstrate a chronically implantable adaptive multielectrode positioning system with detachable drive for computerized depth adjustment of individual electrodes over several millimeters. This semichronic 16-channel system is designed to optimize the simultaneous yield of units in an extended period following implantation since the electrodes can be independently depth adjusted with minimal effort and their signal quality continuously assessed. Importantly, the electrode array is designed to remain within a chronic recording chamber for a prolonged time or can be used for acute recordings with high signal-to-noise ratio in the cerebral cortex of nonhuman primates. NEW & NOTEWORTHY We present a 16-channel motorized, semichronic multielectrode array with individually depth-adjustable electrodes to record in the cerebral cortex of nonhuman primates. Compared with fixed-geometry arrays, this system allows repeated reestablishing of single neuron isolation. Compared with manually adjustable arrays it benefits from computer-controlled positioning. Compared with motorized semichronic systems it allows higher channel counts due to a robotic single actuator approach. Overall the system is designed to optimize the simultaneous yield of units over the course of implantation.

scholarly journals Network dynamics with BrainX3: a large-scale simulation of the human brain network with real-time interaction

2015 ◽  
Vol 9 ◽  
Author(s):  
Xerxes D. Arsiwalla ◽  
Riccardo Zucca ◽  
Alberto Betella ◽  
Enrique Martinez ◽  
David Dalmazzo ◽  
...  
Keyword(s):  
Human Brain ◽  
Real Time ◽  
Large Scale ◽  
Network Dynamics ◽  
Brain Network ◽  
Large Scale Simulation ◽  
Time Interaction

scholarly journals Amplification and Suppression of Distinct Brain-wide Activity Patterns by Catecholamines

2018 ◽  
Author(s):  
RL van den Brink ◽  
S Nieuwenhuis ◽  
TH Donner
Keyword(s):  
Spatial Distribution ◽  
Human Brain ◽  
Spatial Patterns ◽  
Large Scale ◽  
Network Dynamics ◽  
Brain Network ◽  
Heterogeneous Distribution ◽  
Neurotransmitter Systems ◽  
The Brain ◽  
Catecholamine Levels

ABSTRACTThe widely projecting catecholaminergic (norepinephrine and dopamine) neurotransmitter systems profoundly shape the state of neuronal networks in the forebrain. Current models posit that the effects of catecholaminergic modulation on network dynamics are homogenous across the brain. However, the brain is equipped with a variety of catecholamine receptors with distinct functional effects and heterogeneous density across brain regions. Consequently, catecholaminergic effects on brain-wide network dynamics might be more spatially specific than assumed. We tested this idea through the analysis of functional magnetic resonance imaging (fMRI) measurements performed in humans (19 females, 5 males) at ‘rest’ under pharmacological (atomoxetine-induced) elevation of catecholamine levels. We used a linear decomposition technique to identify spatial patterns of correlated fMRI signal fluctuations that were either increased or decreased by atomoxetine. This yielded two distinct spatial patterns, each expressing reliable and specific drug effects. The spatial structure of both fluctuation patterns resembled the spatial distribution of the expression of catecholamine receptor genes: α1 norepinephrine receptors (for the fluctuation pattern: placebo > atomoxetine), ‘D2-like’ dopamine receptors (pattern: atomoxetine > placebo), and β norepinephrine receptors (for both patterns, with correlations of opposite sign). We conclude that catecholaminergic effects on the forebrain are spatially more structured than traditionally assumed and at least in part explained by the heterogeneous distribution of various catecholamine receptors. Our findings link catecholaminergic effects on large-scale brain networks to low-level characteristics of the underlying neurotransmitter systems. They also provide key constraints for the development of realistic models of neuromodulatory effects on large-scale brain network dynamics.SIGNIFICANCE STATEMENTThe catecholamines norepinephrine and dopamine are an important class of modulatory neurotransmitters. Because of the widespread and diffuse release of these neuromodulators, it has commonly been assumed that their effects on neural interactions are homogenous across the brain. Here, we present results from the human brain that challenge this view. We pharmacologically increased catecholamine levels and imaged the effects on the spontaneous covariations between brain-wide fMRI signals at ‘rest’. We identified two distinct spatial patterns of covariations: one that was amplified and another that was suppressed by catecholamines. Each pattern was associated with the heterogeneous spatial distribution of the expression of distinct catecholamine receptor genes. Our results provide novel insights into the catecholaminergic modulation of large-scale human brain dynamics.

scholarly journals Large-scale network integration in the human brain tracks temporal fluctuations in memory encoding performance

eLife ◽  
2018 ◽  
Vol 7 ◽  
Author(s):  
Ruedeerat Keerativittayayut ◽  
Ryuta Aoki ◽  
Mitra Taghizadeh Sarabi ◽  
Koji Jimura ◽  
Kiyoshi Nakahara
Keyword(s):  
Functional Connectivity ◽  
Human Brain ◽  
Large Scale ◽  
Brain Regions ◽  
Brain Network ◽  
Time Varying ◽  
Memory Encoding ◽  
Graph Metrics ◽  
Connectivity Patterns ◽  
Network States

Although activation/deactivation of specific brain regions has been shown to be predictive of successful memory encoding, the relationship between time-varying large-scale brain networks and fluctuations of memory encoding performance remains unclear. Here, we investigated time-varying functional connectivity patterns across the human brain in periods of 30–40 s, which have recently been implicated in various cognitive functions. During functional magnetic resonance imaging, participants performed a memory encoding task, and their performance was assessed with a subsequent surprise memory test. A graph analysis of functional connectivity patterns revealed that increased integration of the subcortical, default-mode, salience, and visual subnetworks with other subnetworks is a hallmark of successful memory encoding. Moreover, multivariate analysis using the graph metrics of integration reliably classified the brain network states into the period of high (vs. low) memory encoding performance. Our findings suggest that a diverse set of brain systems dynamically interact to support successful memory encoding.

scholarly journals Large-Scale Functional Brain Network Architecture Changes Associated With Trauma-Related Dissociation

2020 ◽  
pp. appi.ajp.2020.1
Author(s):  
Lauren A.M. Lebois ◽  
Meiling Li ◽  
Justin T. Baker ◽  
Jonathan D. Wolff ◽  
Danhong Wang ◽  
...  
Keyword(s):  
Network Architecture ◽  
Large Scale ◽  
Brain Network ◽  
Functional Brain ◽  
Functional Brain Network

scholarly journals Identification of neural oscillations and epileptiform changes in human brain organoids

2019 ◽  
Author(s):  
Ranmal A. Samarasinghe ◽  
Osvaldo A. Miranda ◽  
Simon Mitchell ◽  
Isabella Ferando ◽  
Momoko Watanabe ◽  
...  
Keyword(s):  
Neural Network ◽  
Human Brain ◽  
Network Architecture ◽  
Network Formation ◽  
Neurological Diseases ◽  
Extracellular Recording ◽  
Brain Network ◽  
Network Activity ◽  
Intact Brain ◽  
Oscillatory Network

ABSTRACTHuman brain organoids represent a powerful tool for the study of human neurological diseases particularly those that impact brain growth and structure. However, many neurological diseases lack obvious anatomical abnormalities, yet significantly impact neural network functions, raising the question of whether organoids possess sufficient neural network architecture and complexity to model these conditions. Here, we explore the network level functions of brain organoids using calcium sensor imaging and extracellular recording approaches that together reveal the existence of complex oscillatory network behaviors reminiscent of intact brain preparations. We further demonstrate strikingly abnormal epileptiform network activity in organoids derived from a Rett Syndrome patient despite only modest anatomical differences from isogenically matched controls, and rescue with an unconventional neuromodulatory drug Pifithrin-α. Together, these findings provide an essential foundation for the utilization of human brain organoids to study intact and disordered human brain network formation and illustrate their utility in therapeutic discovery.

55. Large-Scale Brain Network Architecture Changes Associated With a Trauma-Related Dissociative Syndrome

2019 ◽  
Vol 85 (10) ◽  
pp. S23
Author(s):  
Lauren Lebois ◽  
Meiling Li ◽  
Jonathan Wolff ◽  
Danhong Wang ◽  
Liz Grinspoon ◽  
...  
Keyword(s):  
Network Architecture ◽  
Large Scale ◽  
Brain Network

scholarly journals Generative models of rich clubs in Hebbian neuronal networks and large-scale human brain networks

2014 ◽  
Vol 369 (1653) ◽  
pp. 20130531 ◽  
Author(s):  
Petra E. Vértes ◽  
Aaron Alexander-Bloch ◽  
Edward T. Bullmore
Keyword(s):  
Neural Networks ◽  
Human Brain ◽  
Large Scale ◽  
Hebbian Learning ◽  
Brain Connectivity ◽  
Brain Networks ◽  
Brain Network ◽  
Generative Models ◽  
Adaptive Behaviour ◽  
Topological Term

Rich clubs arise when nodes that are ‘rich’ in connections also form an elite, densely connected ‘club’. In brain networks, rich clubs incur high physical connection costs but also appear to be especially valuable to brain function. However, little is known about the selection pressures that drive their formation. Here, we take two complementary approaches to this question: firstly we show, using generative modelling, that the emergence of rich clubs in large-scale human brain networks can be driven by an economic trade-off between connection costs and a second, competing topological term. Secondly we show, using simulated neural networks, that Hebbian learning rules also drive the emergence of rich clubs at the microscopic level, and that the prominence of these features increases with learning time. These results suggest that Hebbian learning may provide a neuronal mechanism for the selection of complex features such as rich clubs. The neural networks that we investigate are explicitly Hebbian, and we argue that the topological term in our model of large-scale brain connectivity may represent an analogous connection rule. This putative link between learning and rich clubs is also consistent with predictions that integrative aspects of brain network organization are especially important for adaptive behaviour.

scholarly journals Mapping human brain networks with cortico-cortical evoked potentials

2014 ◽  
Vol 369 (1653) ◽  
pp. 20130528 ◽  
Author(s):  
Corey J. Keller ◽  
Christopher J. Honey ◽  
Pierre Mégevand ◽  
Laszlo Entz ◽  
Istvan Ulbert ◽  
...  
Keyword(s):  
Cerebral Cortex ◽  
Cognitive Processing ◽  
Cognitive Abilities ◽  
Large Scale ◽  
Effective Connectivity ◽  
Brain Networks ◽  
Brain Network ◽  
Fundamental Aspect ◽  
Neural Basis ◽  
Network Function

The cerebral cortex forms a sheet of neurons organized into a network of interconnected modules that is highly expanded in humans and presumably enables our most refined sensory and cognitive abilities. The links of this network form a fundamental aspect of its organization, and a great deal of research is focusing on understanding how information flows within and between different regions. However, an often-overlooked element of this connectivity regards a causal, hierarchical structure of regions, whereby certain nodes of the cortical network may exert greater influence over the others. While this is difficult to ascertain non-invasively, patients undergoing invasive electrode monitoring for epilepsy provide a unique window into this aspect of cortical organization. In this review, we highlight the potential for cortico-cortical evoked potential (CCEP) mapping to directly measure neuronal propagation across large-scale brain networks with spatio-temporal resolution that is superior to traditional neuroimaging methods. We first introduce effective connectivity and discuss the mechanisms underlying CCEP generation. Next, we highlight how CCEP mapping has begun to provide insight into the neural basis of non-invasive imaging signals. Finally, we present a novel approach to perturbing and measuring brain network function during cognitive processing. The direct measurement of CCEPs in response to electrical stimulation represents a potentially powerful clinical and basic science tool for probing the large-scale networks of the human cerebral cortex.

scholarly journals Spatial multi-scaled chimera states of cerebral cortex network and its inherent structure-dynamics relationship in human brain

2020 ◽  
Author(s):  
S Huo ◽  
C Tian ◽  
M Zheng ◽  
S Guan ◽  
C S Zhou ◽  
...  
Keyword(s):  
Cerebral Cortex ◽  
Human Brain ◽  
Spatial Scales ◽  
Brain Network ◽  
Chimera States ◽  
Hierarchical Trees ◽  
Neural Mass ◽  
Functional Patterns ◽  
New Feature ◽  
Global And Local

Abstract Human cerebral cortex displays various dynamics patterns under different states, however the mechanism how such diverse patterns can be supported by the underlying brain network is still not well understood. Human brain has a unique network structure with different regions of interesting to perform cognitive tasks. Using coupled neural mass oscillators on human cortical network and paying attention to both global and local regions, we observe a new feature of chimera states with multiple spatial scales and a positive correlation between the synchronization preference of local region and the degree of symmetry of the connectivity of the region in the network. Further, we use the concept of effective symmetry in the network to build structural and dynamical hierarchical trees and find a close matching between them. These results help to explain the multiple brain rhythms observed in experiments and suggest a generic principle for complex brain network as a structure substrate to support diverse functional patterns.

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